FPL.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "e29d4059",
   "metadata": {},
   "outputs": [],
   "source": [
    "# setting up notebook\n",
    "import requests\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "d6d681e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['events', 'game_settings', 'phases', 'teams', 'total_players', 'elements', 'element_stats', 'element_types'])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#API Set-Up\n",
    "url = 'https://fantasy.premierleague.com/api/bootstrap-static/'\n",
    "r = requests.get(url)\n",
    "json = r.json()\n",
    "json.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "0d5bf1c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chance_of_playing_next_round</th>\n",
       "      <th>chance_of_playing_this_round</th>\n",
       "      <th>code</th>\n",
       "      <th>cost_change_event</th>\n",
       "      <th>cost_change_event_fall</th>\n",
       "      <th>cost_change_start</th>\n",
       "      <th>cost_change_start_fall</th>\n",
       "      <th>dreamteam_count</th>\n",
       "      <th>element_type</th>\n",
       "      <th>ep_next</th>\n",
       "      <th>...</th>\n",
       "      <th>threat_rank</th>\n",
       "      <th>threat_rank_type</th>\n",
       "      <th>ict_index_rank</th>\n",
       "      <th>ict_index_rank_type</th>\n",
       "      <th>corners_and_indirect_freekicks_order</th>\n",
       "      <th>corners_and_indirect_freekicks_text</th>\n",
       "      <th>direct_freekicks_order</th>\n",
       "      <th>direct_freekicks_text</th>\n",
       "      <th>penalties_order</th>\n",
       "      <th>penalties_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37605</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>687</td>\n",
       "      <td>279</td>\n",
       "      <td>687</td>\n",
       "      <td>279</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39476</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>634</td>\n",
       "      <td>230</td>\n",
       "      <td>653</td>\n",
       "      <td>232</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41270</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>221</td>\n",
       "      <td>62</td>\n",
       "      <td>276</td>\n",
       "      <td>90</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>4.0</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>54694</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-6</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>28</td>\n",
       "      <td>15</td>\n",
       "      <td>41</td>\n",
       "      <td>26</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1.0</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>58822</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>...</td>\n",
       "      <td>299</td>\n",
       "      <td>97</td>\n",
       "      <td>326</td>\n",
       "      <td>115</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>5.0</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>448487</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>490</td>\n",
       "      <td>28</td>\n",
       "      <td>544</td>\n",
       "      <td>61</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>683</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>209353</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>420</td>\n",
       "      <td>65</td>\n",
       "      <td>491</td>\n",
       "      <td>67</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>465551</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>494</td>\n",
       "      <td>196</td>\n",
       "      <td>548</td>\n",
       "      <td>208</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>73314</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.3</td>\n",
       "      <td>...</td>\n",
       "      <td>203</td>\n",
       "      <td>49</td>\n",
       "      <td>331</td>\n",
       "      <td>46</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>686</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>490721</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>498</td>\n",
       "      <td>199</td>\n",
       "      <td>552</td>\n",
       "      <td>211</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>687 rows × 67 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     chance_of_playing_next_round  chance_of_playing_this_round    code  \\\n",
       "0                             0.0                           0.0   37605   \n",
       "1                             0.0                           0.0   39476   \n",
       "2                             0.0                           0.0   41270   \n",
       "3                           100.0                         100.0   54694   \n",
       "4                           100.0                         100.0   58822   \n",
       "..                            ...                           ...     ...   \n",
       "682                           NaN                           NaN  448487   \n",
       "683                           0.0                           0.0  209353   \n",
       "684                           NaN                           NaN  465551   \n",
       "685                         100.0                         100.0   73314   \n",
       "686                           NaN                           NaN  490721   \n",
       "\n",
       "     cost_change_event  cost_change_event_fall  cost_change_start  \\\n",
       "0                    0                       0                 -3   \n",
       "1                    0                       0                 -2   \n",
       "2                    0                       0                 -1   \n",
       "3                    0                       0                 -6   \n",
       "4                    0                       0                 -4   \n",
       "..                 ...                     ...                ...   \n",
       "682                  0                       0                  0   \n",
       "683                  0                       0                 -1   \n",
       "684                  0                       0                  0   \n",
       "685                  0                       0                 -2   \n",
       "686                  0                       0                  0   \n",
       "\n",
       "     cost_change_start_fall  dreamteam_count  element_type ep_next  ...  \\\n",
       "0                         3                0             3     0.0  ...   \n",
       "1                         2                0             2     0.0  ...   \n",
       "2                         1                0             2     0.0  ...   \n",
       "3                         6                2             3     4.0  ...   \n",
       "4                         4                0             2     1.5  ...   \n",
       "..                      ...              ...           ...     ...  ...   \n",
       "682                       0                0             1     0.0  ...   \n",
       "683                       1                0             4     0.0  ...   \n",
       "684                       0                0             2     0.0  ...   \n",
       "685                       2                0             4     1.3  ...   \n",
       "686                       0                0             2     0.0  ...   \n",
       "\n",
       "    threat_rank  threat_rank_type ict_index_rank ict_index_rank_type  \\\n",
       "0           687               279            687                 279   \n",
       "1           634               230            653                 232   \n",
       "2           221                62            276                  90   \n",
       "3            28                15             41                  26   \n",
       "4           299                97            326                 115   \n",
       "..          ...               ...            ...                 ...   \n",
       "682         490                28            544                  61   \n",
       "683         420                65            491                  67   \n",
       "684         494               196            548                 208   \n",
       "685         203                49            331                  46   \n",
       "686         498               199            552                 211   \n",
       "\n",
       "     corners_and_indirect_freekicks_order  \\\n",
       "0                                     NaN   \n",
       "1                                     NaN   \n",
       "2                                     NaN   \n",
       "3                                     NaN   \n",
       "4                                     NaN   \n",
       "..                                    ...   \n",
       "682                                   NaN   \n",
       "683                                   NaN   \n",
       "684                                   NaN   \n",
       "685                                   NaN   \n",
       "686                                   NaN   \n",
       "\n",
       "     corners_and_indirect_freekicks_text direct_freekicks_order  \\\n",
       "0                                                           NaN   \n",
       "1                                                           NaN   \n",
       "2                                                           4.0   \n",
       "3                                                           NaN   \n",
       "4                                                           5.0   \n",
       "..                                   ...                    ...   \n",
       "682                                                         NaN   \n",
       "683                                                         NaN   \n",
       "684                                                         NaN   \n",
       "685                                                         NaN   \n",
       "686                                                         NaN   \n",
       "\n",
       "    direct_freekicks_text  penalties_order penalties_text  \n",
       "0                                      NaN                 \n",
       "1                                      NaN                 \n",
       "2                                      NaN                 \n",
       "3                                      1.0                 \n",
       "4                                      NaN                 \n",
       "..                    ...              ...            ...  \n",
       "682                                    NaN                 \n",
       "683                                    NaN                 \n",
       "684                                    NaN                 \n",
       "685                                    NaN                 \n",
       "686                                    NaN                 \n",
       "\n",
       "[687 rows x 67 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#elements are the different players\n",
    "elements_df = pd.DataFrame(json['elements'])\n",
    "element_types_df = pd.DataFrame(json['element_types'])\n",
    "teams_df = pd.DataFrame(json['teams'])\n",
    "elements_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "f1b21931",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['chance_of_playing_next_round', 'chance_of_playing_this_round', 'code',\n",
       "       'cost_change_event', 'cost_change_event_fall', 'cost_change_start',\n",
       "       'cost_change_start_fall', 'dreamteam_count', 'element_type', 'ep_next',\n",
       "       'ep_this', 'event_points', 'first_name', 'form', 'id', 'in_dreamteam',\n",
       "       'news', 'news_added', 'now_cost', 'photo', 'points_per_game',\n",
       "       'second_name', 'selected_by_percent', 'special', 'squad_number',\n",
       "       'status', 'team', 'team_code', 'total_points', 'transfers_in',\n",
       "       'transfers_in_event', 'transfers_out', 'transfers_out_event',\n",
       "       'value_form', 'value_season', 'web_name', 'minutes', 'goals_scored',\n",
       "       'assists', 'clean_sheets', 'goals_conceded', 'own_goals',\n",
       "       'penalties_saved', 'penalties_missed', 'yellow_cards', 'red_cards',\n",
       "       'saves', 'bonus', 'bps', 'influence', 'creativity', 'threat',\n",
       "       'ict_index', 'influence_rank', 'influence_rank_type', 'creativity_rank',\n",
       "       'creativity_rank_type', 'threat_rank', 'threat_rank_type',\n",
       "       'ict_index_rank', 'ict_index_rank_type',\n",
       "       'corners_and_indirect_freekicks_order',\n",
       "       'corners_and_indirect_freekicks_text', 'direct_freekicks_order',\n",
       "       'direct_freekicks_text', 'penalties_order', 'penalties_text'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#looking at which columns I will use.b\n",
    "elements_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "f2fe04d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>web_name</th>\n",
       "      <th>first_name</th>\n",
       "      <th>team</th>\n",
       "      <th>element_type</th>\n",
       "      <th>now_cost</th>\n",
       "      <th>selected_by_percent</th>\n",
       "      <th>transfers_in</th>\n",
       "      <th>transfers_out</th>\n",
       "      <th>form</th>\n",
       "      <th>event_points</th>\n",
       "      <th>total_points</th>\n",
       "      <th>bonus</th>\n",
       "      <th>points_per_game</th>\n",
       "      <th>value_season</th>\n",
       "      <th>minutes</th>\n",
       "      <th>goals_scored</th>\n",
       "      <th>assists</th>\n",
       "      <th>ict_index</th>\n",
       "      <th>clean_sheets</th>\n",
       "      <th>saves</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Özil</td>\n",
       "      <td>Mesut</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>67</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3441</td>\n",
       "      <td>54702</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sokratis</td>\n",
       "      <td>Sokratis</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>48</td>\n",
       "      <td>0.1</td>\n",
       "      <td>10266</td>\n",
       "      <td>19112</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>David Luiz</td>\n",
       "      <td>David</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>54</td>\n",
       "      <td>0.9</td>\n",
       "      <td>76447</td>\n",
       "      <td>120122</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>7.4</td>\n",
       "      <td>1344</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aubameyang</td>\n",
       "      <td>Pierre-Emerick</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>114</td>\n",
       "      <td>9.4</td>\n",
       "      <td>1116383</td>\n",
       "      <td>3574493</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>108</td>\n",
       "      <td>10</td>\n",
       "      <td>4.3</td>\n",
       "      <td>9.5</td>\n",
       "      <td>2007</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>144.9</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cédric</td>\n",
       "      <td>Cédric</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>46</td>\n",
       "      <td>0.3</td>\n",
       "      <td>29526</td>\n",
       "      <td>52537</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>744</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>29.2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>Söndergaard</td>\n",
       "      <td>Andreas</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>0.3</td>\n",
       "      <td>31611</td>\n",
       "      <td>15736</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>683</th>\n",
       "      <td>Cutrone</td>\n",
       "      <td>Patrick</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>59</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3443</td>\n",
       "      <td>3419</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>Lonwijk</td>\n",
       "      <td>Nigel</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1275</td>\n",
       "      <td>981</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>Willian José</td>\n",
       "      <td>Willian José</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>68</td>\n",
       "      <td>0.2</td>\n",
       "      <td>45569</td>\n",
       "      <td>36387</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>660</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>27.8</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>686</th>\n",
       "      <td>Bueno</td>\n",
       "      <td>Hugo</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1568</td>\n",
       "      <td>942</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>687 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         web_name      first_name  team  element_type  now_cost  \\\n",
       "0            Özil           Mesut     1             3        67   \n",
       "1        Sokratis        Sokratis     1             2        48   \n",
       "2      David Luiz           David     1             2        54   \n",
       "3      Aubameyang  Pierre-Emerick     1             3       114   \n",
       "4          Cédric          Cédric     1             2        46   \n",
       "..            ...             ...   ...           ...       ...   \n",
       "682   Söndergaard         Andreas    20             1        40   \n",
       "683       Cutrone         Patrick    20             4        59   \n",
       "684       Lonwijk           Nigel    20             2        40   \n",
       "685  Willian José    Willian José    20             4        68   \n",
       "686         Bueno            Hugo    20             2        40   \n",
       "\n",
       "    selected_by_percent  transfers_in  transfers_out form  event_points  \\\n",
       "0                   0.5          3441          54702  0.0             0   \n",
       "1                   0.1         10266          19112  0.0             0   \n",
       "2                   0.9         76447         120122  1.2             0   \n",
       "3                   9.4       1116383        3574493  3.0             2   \n",
       "4                   0.3         29526          52537  0.5             0   \n",
       "..                  ...           ...            ...  ...           ...   \n",
       "682                 0.3         31611          15736  0.0             0   \n",
       "683                 0.0          3443           3419  0.0             0   \n",
       "684                 0.0          1275            981  0.0             0   \n",
       "685                 0.2         45569          36387  1.3             0   \n",
       "686                 0.0          1568            942  0.0             0   \n",
       "\n",
       "     total_points  bonus points_per_game value_season  minutes  goals_scored  \\\n",
       "0               0      0             0.0          0.0        0             0   \n",
       "1               0      0             0.0          0.0        0             0   \n",
       "2              40      0             2.1          7.4     1344             1   \n",
       "3             108     10             4.3          9.5     2007             9   \n",
       "4              28      3             2.8          6.1      744             0   \n",
       "..            ...    ...             ...          ...      ...           ...   \n",
       "682             0      0             0.0          0.0        0             0   \n",
       "683             2      0             1.0          0.3       23             0   \n",
       "684             0      0             0.0          0.0        0             0   \n",
       "685            20      0             2.2          2.9      660             0   \n",
       "686             0      0             0.0          0.0        0             0   \n",
       "\n",
       "     assists ict_index  clean_sheets  saves  \n",
       "0          0       0.0             0      0  \n",
       "1          0       0.0             0      0  \n",
       "2          0      41.0             3      0  \n",
       "3          1     144.9             7      0  \n",
       "4          1      29.2             2      0  \n",
       "..       ...       ...           ...    ...  \n",
       "682        0       0.0             0      0  \n",
       "683        0       0.2             0      0  \n",
       "684        0       0.0             0      0  \n",
       "685        1      27.8             3      0  \n",
       "686        0       0.0             0      0  \n",
       "\n",
       "[687 rows x 20 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating a data frame with important column called main_df\n",
    "main_df = elements_df[\n",
    "    ['web_name','first_name','team','element_type','now_cost','selected_by_percent','transfers_in','transfers_out','form','event_points','total_points','bonus','points_per_game','value_season','minutes','goals_scored','assists','ict_index','clean_sheets','saves']\n",
    "]\n",
    "main_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "a820761e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>draw</th>\n",
       "      <th>form</th>\n",
       "      <th>id</th>\n",
       "      <th>loss</th>\n",
       "      <th>name</th>\n",
       "      <th>played</th>\n",
       "      <th>points</th>\n",
       "      <th>position</th>\n",
       "      <th>short_name</th>\n",
       "      <th>...</th>\n",
       "      <th>team_division</th>\n",
       "      <th>unavailable</th>\n",
       "      <th>win</th>\n",
       "      <th>strength_overall_home</th>\n",
       "      <th>strength_overall_away</th>\n",
       "      <th>strength_attack_home</th>\n",
       "      <th>strength_attack_away</th>\n",
       "      <th>strength_defence_home</th>\n",
       "      <th>strength_defence_away</th>\n",
       "      <th>pulse_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>ARS</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1190</td>\n",
       "      <td>1210</td>\n",
       "      <td>1170</td>\n",
       "      <td>1210</td>\n",
       "      <td>1190</td>\n",
       "      <td>1200</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Aston Villa</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>AVL</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1150</td>\n",
       "      <td>1150</td>\n",
       "      <td>1150</td>\n",
       "      <td>1150</td>\n",
       "      <td>1180</td>\n",
       "      <td>1210</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Brighton</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BHA</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1120</td>\n",
       "      <td>1100</td>\n",
       "      <td>1150</td>\n",
       "      <td>1180</td>\n",
       "      <td>1090</td>\n",
       "      <td>1100</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>Burnley</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BUR</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1070</td>\n",
       "      <td>1080</td>\n",
       "      <td>1120</td>\n",
       "      <td>1190</td>\n",
       "      <td>1010</td>\n",
       "      <td>1030</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>Chelsea</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>CHE</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1250</td>\n",
       "      <td>1300</td>\n",
       "      <td>1240</td>\n",
       "      <td>1280</td>\n",
       "      <td>1270</td>\n",
       "      <td>1310</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>CRY</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1120</td>\n",
       "      <td>1110</td>\n",
       "      <td>1100</td>\n",
       "      <td>1150</td>\n",
       "      <td>1020</td>\n",
       "      <td>1050</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>Everton</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>EVE</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1240</td>\n",
       "      <td>1160</td>\n",
       "      <td>1150</td>\n",
       "      <td>1170</td>\n",
       "      <td>1210</td>\n",
       "      <td>1250</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>Fulham</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>FUL</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1090</td>\n",
       "      <td>1100</td>\n",
       "      <td>1020</td>\n",
       "      <td>1030</td>\n",
       "      <td>1020</td>\n",
       "      <td>1020</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>Leicester</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>LEI</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1240</td>\n",
       "      <td>1270</td>\n",
       "      <td>1190</td>\n",
       "      <td>1190</td>\n",
       "      <td>1200</td>\n",
       "      <td>1180</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>Leeds</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>LEE</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1130</td>\n",
       "      <td>1170</td>\n",
       "      <td>1060</td>\n",
       "      <td>1110</td>\n",
       "      <td>1130</td>\n",
       "      <td>1160</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>LIV</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1250</td>\n",
       "      <td>1270</td>\n",
       "      <td>1240</td>\n",
       "      <td>1320</td>\n",
       "      <td>1330</td>\n",
       "      <td>1350</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>Man City</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>MCI</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1350</td>\n",
       "      <td>1360</td>\n",
       "      <td>1260</td>\n",
       "      <td>1320</td>\n",
       "      <td>1330</td>\n",
       "      <td>1350</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>Man Utd</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>MUN</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1260</td>\n",
       "      <td>1320</td>\n",
       "      <td>1220</td>\n",
       "      <td>1230</td>\n",
       "      <td>1250</td>\n",
       "      <td>1260</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>Newcastle</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NEW</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1090</td>\n",
       "      <td>1120</td>\n",
       "      <td>1100</td>\n",
       "      <td>1120</td>\n",
       "      <td>1010</td>\n",
       "      <td>1060</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>Sheffield Utd</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SHU</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1040</td>\n",
       "      <td>1070</td>\n",
       "      <td>1110</td>\n",
       "      <td>1130</td>\n",
       "      <td>1010</td>\n",
       "      <td>1050</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOU</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1080</td>\n",
       "      <td>1110</td>\n",
       "      <td>1140</td>\n",
       "      <td>1200</td>\n",
       "      <td>1110</td>\n",
       "      <td>1160</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>Spurs</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>TOT</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1200</td>\n",
       "      <td>1260</td>\n",
       "      <td>1190</td>\n",
       "      <td>1240</td>\n",
       "      <td>1280</td>\n",
       "      <td>1320</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>West Brom</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>WBA</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1020</td>\n",
       "      <td>1050</td>\n",
       "      <td>1020</td>\n",
       "      <td>1020</td>\n",
       "      <td>1000</td>\n",
       "      <td>1010</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>West Ham</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>WHU</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1220</td>\n",
       "      <td>1260</td>\n",
       "      <td>1150</td>\n",
       "      <td>1160</td>\n",
       "      <td>1170</td>\n",
       "      <td>1180</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>Wolves</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>WOL</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1130</td>\n",
       "      <td>1170</td>\n",
       "      <td>1180</td>\n",
       "      <td>1220</td>\n",
       "      <td>1100</td>\n",
       "      <td>1170</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    code  draw  form  id  loss            name  played  points  position  \\\n",
       "0      3     0  None   1     0         Arsenal       0       0         0   \n",
       "1      7     0  None   2     0     Aston Villa       0       0         0   \n",
       "2     36     0  None   3     0        Brighton       0       0         0   \n",
       "3     90     0  None   4     0         Burnley       0       0         0   \n",
       "4      8     0  None   5     0         Chelsea       0       0         0   \n",
       "5     31     0  None   6     0  Crystal Palace       0       0         0   \n",
       "6     11     0  None   7     0         Everton       0       0         0   \n",
       "7     54     0  None   8     0          Fulham       0       0         0   \n",
       "8     13     0  None   9     0       Leicester       0       0         0   \n",
       "9      2     0  None  10     0           Leeds       0       0         0   \n",
       "10    14     0  None  11     0       Liverpool       0       0         0   \n",
       "11    43     0  None  12     0        Man City       0       0         0   \n",
       "12     1     0  None  13     0         Man Utd       0       0         0   \n",
       "13     4     0  None  14     0       Newcastle       0       0         0   \n",
       "14    49     0  None  15     0   Sheffield Utd       0       0         0   \n",
       "15    20     0  None  16     0     Southampton       0       0         0   \n",
       "16     6     0  None  17     0           Spurs       0       0         0   \n",
       "17    35     0  None  18     0       West Brom       0       0         0   \n",
       "18    21     0  None  19     0        West Ham       0       0         0   \n",
       "19    39     0  None  20     0          Wolves       0       0         0   \n",
       "\n",
       "   short_name  ...  team_division unavailable  win  strength_overall_home  \\\n",
       "0         ARS  ...           None       False    0                   1190   \n",
       "1         AVL  ...           None       False    0                   1150   \n",
       "2         BHA  ...           None       False    0                   1120   \n",
       "3         BUR  ...           None       False    0                   1070   \n",
       "4         CHE  ...           None       False    0                   1250   \n",
       "5         CRY  ...           None       False    0                   1120   \n",
       "6         EVE  ...           None       False    0                   1240   \n",
       "7         FUL  ...           None       False    0                   1090   \n",
       "8         LEI  ...           None       False    0                   1240   \n",
       "9         LEE  ...           None       False    0                   1130   \n",
       "10        LIV  ...           None       False    0                   1250   \n",
       "11        MCI  ...           None       False    0                   1350   \n",
       "12        MUN  ...           None       False    0                   1260   \n",
       "13        NEW  ...           None       False    0                   1090   \n",
       "14        SHU  ...           None       False    0                   1040   \n",
       "15        SOU  ...           None       False    0                   1080   \n",
       "16        TOT  ...           None       False    0                   1200   \n",
       "17        WBA  ...           None       False    0                   1020   \n",
       "18        WHU  ...           None       False    0                   1220   \n",
       "19        WOL  ...           None       False    0                   1130   \n",
       "\n",
       "    strength_overall_away  strength_attack_home  strength_attack_away  \\\n",
       "0                    1210                  1170                  1210   \n",
       "1                    1150                  1150                  1150   \n",
       "2                    1100                  1150                  1180   \n",
       "3                    1080                  1120                  1190   \n",
       "4                    1300                  1240                  1280   \n",
       "5                    1110                  1100                  1150   \n",
       "6                    1160                  1150                  1170   \n",
       "7                    1100                  1020                  1030   \n",
       "8                    1270                  1190                  1190   \n",
       "9                    1170                  1060                  1110   \n",
       "10                   1270                  1240                  1320   \n",
       "11                   1360                  1260                  1320   \n",
       "12                   1320                  1220                  1230   \n",
       "13                   1120                  1100                  1120   \n",
       "14                   1070                  1110                  1130   \n",
       "15                   1110                  1140                  1200   \n",
       "16                   1260                  1190                  1240   \n",
       "17                   1050                  1020                  1020   \n",
       "18                   1260                  1150                  1160   \n",
       "19                   1170                  1180                  1220   \n",
       "\n",
       "    strength_defence_home  strength_defence_away  pulse_id  \n",
       "0                    1190                   1200         1  \n",
       "1                    1180                   1210         2  \n",
       "2                    1090                   1100       131  \n",
       "3                    1010                   1030        43  \n",
       "4                    1270                   1310         4  \n",
       "5                    1020                   1050         6  \n",
       "6                    1210                   1250         7  \n",
       "7                    1020                   1020        34  \n",
       "8                    1200                   1180        26  \n",
       "9                    1130                   1160         9  \n",
       "10                   1330                   1350        10  \n",
       "11                   1330                   1350        11  \n",
       "12                   1250                   1260        12  \n",
       "13                   1010                   1060        23  \n",
       "14                   1010                   1050        18  \n",
       "15                   1110                   1160        20  \n",
       "16                   1280                   1320        21  \n",
       "17                   1000                   1010        36  \n",
       "18                   1170                   1180        25  \n",
       "19                   1100                   1170        38  \n",
       "\n",
       "[20 rows x 21 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "teams_df\n",
    "#problem spotted: games played hasn't been updated in the API\n",
    "#easiest solution is to manually insert games played for each team"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "c4dca5cf",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              team games_played\n",
      "0          Arsenal           30\n",
      "1      Aston Villa           29\n",
      "2         Brighton           30\n",
      "3          Burnley           30\n",
      "4          Chelsea           30\n",
      "5   Crystal Palace           29\n",
      "6          Everton           28\n",
      "7           Fulham           31\n",
      "8        Leicester           30\n",
      "9            Leeds           30\n",
      "10       Liverpool           30\n",
      "11        Man City           31\n",
      "12         Man Utd           30\n",
      "13       Newcastle           30\n",
      "14   Sheffield Utd           30\n",
      "15     Southampton           30\n",
      "16           Spurs           30\n",
      "17       West Brom           30\n",
      "18        West Ham           29\n",
      "19          Wolves           29\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>draw</th>\n",
       "      <th>form</th>\n",
       "      <th>id</th>\n",
       "      <th>loss</th>\n",
       "      <th>name</th>\n",
       "      <th>played</th>\n",
       "      <th>points</th>\n",
       "      <th>position</th>\n",
       "      <th>short_name</th>\n",
       "      <th>...</th>\n",
       "      <th>team_division</th>\n",
       "      <th>unavailable</th>\n",
       "      <th>win</th>\n",
       "      <th>strength_overall_home</th>\n",
       "      <th>strength_overall_away</th>\n",
       "      <th>strength_attack_home</th>\n",
       "      <th>strength_attack_away</th>\n",
       "      <th>strength_defence_home</th>\n",
       "      <th>strength_defence_away</th>\n",
       "      <th>pulse_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>ARS</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1190</td>\n",
       "      <td>1210</td>\n",
       "      <td>1170</td>\n",
       "      <td>1210</td>\n",
       "      <td>1190</td>\n",
       "      <td>1200</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Aston Villa</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>AVL</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1150</td>\n",
       "      <td>1150</td>\n",
       "      <td>1150</td>\n",
       "      <td>1150</td>\n",
       "      <td>1180</td>\n",
       "      <td>1210</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Brighton</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BHA</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1120</td>\n",
       "      <td>1100</td>\n",
       "      <td>1150</td>\n",
       "      <td>1180</td>\n",
       "      <td>1090</td>\n",
       "      <td>1100</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>Burnley</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BUR</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1070</td>\n",
       "      <td>1080</td>\n",
       "      <td>1120</td>\n",
       "      <td>1190</td>\n",
       "      <td>1010</td>\n",
       "      <td>1030</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>Chelsea</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>CHE</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1250</td>\n",
       "      <td>1300</td>\n",
       "      <td>1240</td>\n",
       "      <td>1280</td>\n",
       "      <td>1270</td>\n",
       "      <td>1310</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>CRY</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1120</td>\n",
       "      <td>1110</td>\n",
       "      <td>1100</td>\n",
       "      <td>1150</td>\n",
       "      <td>1020</td>\n",
       "      <td>1050</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>Everton</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>EVE</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1240</td>\n",
       "      <td>1160</td>\n",
       "      <td>1150</td>\n",
       "      <td>1170</td>\n",
       "      <td>1210</td>\n",
       "      <td>1250</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>Fulham</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>FUL</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1090</td>\n",
       "      <td>1100</td>\n",
       "      <td>1020</td>\n",
       "      <td>1030</td>\n",
       "      <td>1020</td>\n",
       "      <td>1020</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>Leicester</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>LEI</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1240</td>\n",
       "      <td>1270</td>\n",
       "      <td>1190</td>\n",
       "      <td>1190</td>\n",
       "      <td>1200</td>\n",
       "      <td>1180</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>Leeds</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>LEE</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1130</td>\n",
       "      <td>1170</td>\n",
       "      <td>1060</td>\n",
       "      <td>1110</td>\n",
       "      <td>1130</td>\n",
       "      <td>1160</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>LIV</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1250</td>\n",
       "      <td>1270</td>\n",
       "      <td>1240</td>\n",
       "      <td>1320</td>\n",
       "      <td>1330</td>\n",
       "      <td>1350</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>Man City</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>MCI</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1350</td>\n",
       "      <td>1360</td>\n",
       "      <td>1260</td>\n",
       "      <td>1320</td>\n",
       "      <td>1330</td>\n",
       "      <td>1350</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>Man Utd</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>MUN</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1260</td>\n",
       "      <td>1320</td>\n",
       "      <td>1220</td>\n",
       "      <td>1230</td>\n",
       "      <td>1250</td>\n",
       "      <td>1260</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>Newcastle</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NEW</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1090</td>\n",
       "      <td>1120</td>\n",
       "      <td>1100</td>\n",
       "      <td>1120</td>\n",
       "      <td>1010</td>\n",
       "      <td>1060</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>Sheffield Utd</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SHU</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1040</td>\n",
       "      <td>1070</td>\n",
       "      <td>1110</td>\n",
       "      <td>1130</td>\n",
       "      <td>1010</td>\n",
       "      <td>1050</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOU</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1080</td>\n",
       "      <td>1110</td>\n",
       "      <td>1140</td>\n",
       "      <td>1200</td>\n",
       "      <td>1110</td>\n",
       "      <td>1160</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>Spurs</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>TOT</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1200</td>\n",
       "      <td>1260</td>\n",
       "      <td>1190</td>\n",
       "      <td>1240</td>\n",
       "      <td>1280</td>\n",
       "      <td>1320</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>West Brom</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>WBA</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1020</td>\n",
       "      <td>1050</td>\n",
       "      <td>1020</td>\n",
       "      <td>1020</td>\n",
       "      <td>1000</td>\n",
       "      <td>1010</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>West Ham</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>WHU</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1220</td>\n",
       "      <td>1260</td>\n",
       "      <td>1150</td>\n",
       "      <td>1160</td>\n",
       "      <td>1170</td>\n",
       "      <td>1180</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>Wolves</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>WOL</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1130</td>\n",
       "      <td>1170</td>\n",
       "      <td>1180</td>\n",
       "      <td>1220</td>\n",
       "      <td>1100</td>\n",
       "      <td>1170</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    code  draw  form  id  loss            name  played  points  position  \\\n",
       "0      3     0  None   1     0         Arsenal      30       0         0   \n",
       "1      7     0  None   2     0     Aston Villa      29       0         0   \n",
       "2     36     0  None   3     0        Brighton      30       0         0   \n",
       "3     90     0  None   4     0         Burnley      30       0         0   \n",
       "4      8     0  None   5     0         Chelsea      30       0         0   \n",
       "5     31     0  None   6     0  Crystal Palace      29       0         0   \n",
       "6     11     0  None   7     0         Everton      28       0         0   \n",
       "7     54     0  None   8     0          Fulham      31       0         0   \n",
       "8     13     0  None   9     0       Leicester      30       0         0   \n",
       "9      2     0  None  10     0           Leeds      30       0         0   \n",
       "10    14     0  None  11     0       Liverpool      30       0         0   \n",
       "11    43     0  None  12     0        Man City      31       0         0   \n",
       "12     1     0  None  13     0         Man Utd      30       0         0   \n",
       "13     4     0  None  14     0       Newcastle      30       0         0   \n",
       "14    49     0  None  15     0   Sheffield Utd      30       0         0   \n",
       "15    20     0  None  16     0     Southampton      30       0         0   \n",
       "16     6     0  None  17     0           Spurs      30       0         0   \n",
       "17    35     0  None  18     0       West Brom      30       0         0   \n",
       "18    21     0  None  19     0        West Ham      29       0         0   \n",
       "19    39     0  None  20     0          Wolves      29       0         0   \n",
       "\n",
       "   short_name  ...  team_division unavailable  win  strength_overall_home  \\\n",
       "0         ARS  ...           None       False    0                   1190   \n",
       "1         AVL  ...           None       False    0                   1150   \n",
       "2         BHA  ...           None       False    0                   1120   \n",
       "3         BUR  ...           None       False    0                   1070   \n",
       "4         CHE  ...           None       False    0                   1250   \n",
       "5         CRY  ...           None       False    0                   1120   \n",
       "6         EVE  ...           None       False    0                   1240   \n",
       "7         FUL  ...           None       False    0                   1090   \n",
       "8         LEI  ...           None       False    0                   1240   \n",
       "9         LEE  ...           None       False    0                   1130   \n",
       "10        LIV  ...           None       False    0                   1250   \n",
       "11        MCI  ...           None       False    0                   1350   \n",
       "12        MUN  ...           None       False    0                   1260   \n",
       "13        NEW  ...           None       False    0                   1090   \n",
       "14        SHU  ...           None       False    0                   1040   \n",
       "15        SOU  ...           None       False    0                   1080   \n",
       "16        TOT  ...           None       False    0                   1200   \n",
       "17        WBA  ...           None       False    0                   1020   \n",
       "18        WHU  ...           None       False    0                   1220   \n",
       "19        WOL  ...           None       False    0                   1130   \n",
       "\n",
       "    strength_overall_away  strength_attack_home  strength_attack_away  \\\n",
       "0                    1210                  1170                  1210   \n",
       "1                    1150                  1150                  1150   \n",
       "2                    1100                  1150                  1180   \n",
       "3                    1080                  1120                  1190   \n",
       "4                    1300                  1240                  1280   \n",
       "5                    1110                  1100                  1150   \n",
       "6                    1160                  1150                  1170   \n",
       "7                    1100                  1020                  1030   \n",
       "8                    1270                  1190                  1190   \n",
       "9                    1170                  1060                  1110   \n",
       "10                   1270                  1240                  1320   \n",
       "11                   1360                  1260                  1320   \n",
       "12                   1320                  1220                  1230   \n",
       "13                   1120                  1100                  1120   \n",
       "14                   1070                  1110                  1130   \n",
       "15                   1110                  1140                  1200   \n",
       "16                   1260                  1190                  1240   \n",
       "17                   1050                  1020                  1020   \n",
       "18                   1260                  1150                  1160   \n",
       "19                   1170                  1180                  1220   \n",
       "\n",
       "    strength_defence_home  strength_defence_away  pulse_id  \n",
       "0                    1190                   1200         1  \n",
       "1                    1180                   1210         2  \n",
       "2                    1090                   1100       131  \n",
       "3                    1010                   1030        43  \n",
       "4                    1270                   1310         4  \n",
       "5                    1020                   1050         6  \n",
       "6                    1210                   1250         7  \n",
       "7                    1020                   1020        34  \n",
       "8                    1200                   1180        26  \n",
       "9                    1130                   1160         9  \n",
       "10                   1330                   1350        10  \n",
       "11                   1330                   1350        11  \n",
       "12                   1250                   1260        12  \n",
       "13                   1010                   1060        23  \n",
       "14                   1010                   1050        18  \n",
       "15                   1110                   1160        20  \n",
       "16                   1280                   1320        21  \n",
       "17                   1000                   1010        36  \n",
       "18                   1170                   1180        25  \n",
       "19                   1100                   1170        38  \n",
       "\n",
       "[20 rows x 21 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create new dictionary\n",
    "games_played = [['Arsenal','30'], ['Aston Villa','29'], ['Brighton','30'], ['Burnley','30'], ['Chelsea','30'], ['Crystal Palace','29'], ['Everton','28'], ['Fulham','31'], ['Leicester','30'], ['Leeds','30'], ['Liverpool','30'], ['Man City','31'], ['Man Utd','30'], ['Newcastle','30'], ['Sheffield Utd','30'], ['Southampton','30'], ['Spurs','30'], ['West Brom','30'], ['West Ham','29'], ['Wolves','29']]\n",
    "\n",
    "#turn into a DataFrame\n",
    "played_df = pd.DataFrame(games_played,columns=['team','games_played'])\n",
    "print(played_df)\n",
    "\n",
    "#overwrite existing DataFrame column\n",
    "teams_df['played'] = played_df['games_played'].astype(str).astype(int)\n",
    "\n",
    "#looking good:\n",
    "teams_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "521b788b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>plural_name</th>\n",
       "      <th>plural_name_short</th>\n",
       "      <th>singular_name</th>\n",
       "      <th>singular_name_short</th>\n",
       "      <th>squad_select</th>\n",
       "      <th>squad_min_play</th>\n",
       "      <th>squad_max_play</th>\n",
       "      <th>ui_shirt_specific</th>\n",
       "      <th>sub_positions_locked</th>\n",
       "      <th>element_count</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Goalkeepers</td>\n",
       "      <td>GKP</td>\n",
       "      <td>Goalkeeper</td>\n",
       "      <td>GKP</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>[12]</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Defenders</td>\n",
       "      <td>DEF</td>\n",
       "      <td>Defender</td>\n",
       "      <td>DEF</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Midfielders</td>\n",
       "      <td>MID</td>\n",
       "      <td>Midfielder</td>\n",
       "      <td>MID</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Forwards</td>\n",
       "      <td>FWD</td>\n",
       "      <td>Forward</td>\n",
       "      <td>FWD</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  plural_name plural_name_short singular_name singular_name_short  \\\n",
       "0   1  Goalkeepers               GKP    Goalkeeper                 GKP   \n",
       "1   2    Defenders               DEF      Defender                 DEF   \n",
       "2   3  Midfielders               MID    Midfielder                 MID   \n",
       "3   4     Forwards               FWD       Forward                 FWD   \n",
       "\n",
       "   squad_select  squad_min_play  squad_max_play  ui_shirt_specific  \\\n",
       "0             2               1               1               True   \n",
       "1             5               3               5              False   \n",
       "2             5               2               5              False   \n",
       "3             3               1               3              False   \n",
       "\n",
       "  sub_positions_locked  element_count  \n",
       "0                 [12]             80  \n",
       "1                   []            239  \n",
       "2                   []            279  \n",
       "3                   []             89  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# aim is to merdge this df to the main df\n",
    "element_types_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "35e02118",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['web_name', 'first_name', 'team', 'element_type', 'now_cost',\n",
      "       'selected_by_percent', 'transfers_in', 'transfers_out', 'form',\n",
      "       'event_points', 'total_points', 'bonus', 'points_per_game',\n",
      "       'value_season', 'minutes', 'goals_scored', 'assists', 'ict_index',\n",
      "       'clean_sheets', 'saves', 'id', 'singular_name'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>web_name</th>\n",
       "      <th>first_name</th>\n",
       "      <th>team</th>\n",
       "      <th>now_cost</th>\n",
       "      <th>selected_by_percent</th>\n",
       "      <th>transfers_in</th>\n",
       "      <th>transfers_out</th>\n",
       "      <th>form</th>\n",
       "      <th>event_points</th>\n",
       "      <th>total_points</th>\n",
       "      <th>bonus</th>\n",
       "      <th>points_per_game</th>\n",
       "      <th>value_season</th>\n",
       "      <th>minutes</th>\n",
       "      <th>goals_scored</th>\n",
       "      <th>assists</th>\n",
       "      <th>ict_index</th>\n",
       "      <th>clean_sheets</th>\n",
       "      <th>saves</th>\n",
       "      <th>position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Özil</td>\n",
       "      <td>Mesut</td>\n",
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       "      <td>Midfielder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sokratis</td>\n",
       "      <td>Sokratis</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Defender</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>David Luiz</td>\n",
       "      <td>David</td>\n",
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       "      <td>0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Defender</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aubameyang</td>\n",
       "      <td>Pierre-Emerick</td>\n",
       "      <td>1</td>\n",
       "      <td>114</td>\n",
       "      <td>9.4</td>\n",
       "      <td>1116383</td>\n",
       "      <td>3574493</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>108</td>\n",
       "      <td>10</td>\n",
       "      <td>4.3</td>\n",
       "      <td>9.5</td>\n",
       "      <td>2007</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>144.9</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>Midfielder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cédric</td>\n",
       "      <td>Cédric</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>0.3</td>\n",
       "      <td>29526</td>\n",
       "      <td>52537</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>2.8</td>\n",
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       "      <td>744</td>\n",
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       "      <td>1</td>\n",
       "      <td>29.2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Defender</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>Söndergaard</td>\n",
       "      <td>Andreas</td>\n",
       "      <td>20</td>\n",
       "      <td>40</td>\n",
       "      <td>0.3</td>\n",
       "      <td>31611</td>\n",
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       "      <td>0</td>\n",
       "      <td>Goalkeeper</td>\n",
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       "    <tr>\n",
       "      <th>683</th>\n",
       "      <td>Cutrone</td>\n",
       "      <td>Patrick</td>\n",
       "      <td>20</td>\n",
       "      <td>59</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3443</td>\n",
       "      <td>3419</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Forward</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>Lonwijk</td>\n",
       "      <td>Nigel</td>\n",
       "      <td>20</td>\n",
       "      <td>40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1275</td>\n",
       "      <td>981</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Defender</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>Willian José</td>\n",
       "      <td>Willian José</td>\n",
       "      <td>20</td>\n",
       "      <td>68</td>\n",
       "      <td>0.2</td>\n",
       "      <td>45569</td>\n",
       "      <td>36387</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>660</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>27.8</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Forward</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>686</th>\n",
       "      <td>Bueno</td>\n",
       "      <td>Hugo</td>\n",
       "      <td>20</td>\n",
       "      <td>40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1568</td>\n",
       "      <td>942</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Defender</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>687 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         web_name      first_name  team  now_cost selected_by_percent  \\\n",
       "0            Özil           Mesut     1        67                 0.5   \n",
       "1        Sokratis        Sokratis     1        48                 0.1   \n",
       "2      David Luiz           David     1        54                 0.9   \n",
       "3      Aubameyang  Pierre-Emerick     1       114                 9.4   \n",
       "4          Cédric          Cédric     1        46                 0.3   \n",
       "..            ...             ...   ...       ...                 ...   \n",
       "682   Söndergaard         Andreas    20        40                 0.3   \n",
       "683       Cutrone         Patrick    20        59                 0.0   \n",
       "684       Lonwijk           Nigel    20        40                 0.0   \n",
       "685  Willian José    Willian José    20        68                 0.2   \n",
       "686         Bueno            Hugo    20        40                 0.0   \n",
       "\n",
       "     transfers_in  transfers_out form  event_points  total_points  bonus  \\\n",
       "0            3441          54702  0.0             0             0      0   \n",
       "1           10266          19112  0.0             0             0      0   \n",
       "2           76447         120122  1.2             0            40      0   \n",
       "3         1116383        3574493  3.0             2           108     10   \n",
       "4           29526          52537  0.5             0            28      3   \n",
       "..            ...            ...  ...           ...           ...    ...   \n",
       "682         31611          15736  0.0             0             0      0   \n",
       "683          3443           3419  0.0             0             2      0   \n",
       "684          1275            981  0.0             0             0      0   \n",
       "685         45569          36387  1.3             0            20      0   \n",
       "686          1568            942  0.0             0             0      0   \n",
       "\n",
       "    points_per_game value_season  minutes  goals_scored  assists ict_index  \\\n",
       "0               0.0          0.0        0             0        0       0.0   \n",
       "1               0.0          0.0        0             0        0       0.0   \n",
       "2               2.1          7.4     1344             1        0      41.0   \n",
       "3               4.3          9.5     2007             9        1     144.9   \n",
       "4               2.8          6.1      744             0        1      29.2   \n",
       "..              ...          ...      ...           ...      ...       ...   \n",
       "682             0.0          0.0        0             0        0       0.0   \n",
       "683             1.0          0.3       23             0        0       0.2   \n",
       "684             0.0          0.0        0             0        0       0.0   \n",
       "685             2.2          2.9      660             0        1      27.8   \n",
       "686             0.0          0.0        0             0        0       0.0   \n",
       "\n",
       "     clean_sheets  saves    position  \n",
       "0               0      0  Midfielder  \n",
       "1               0      0    Defender  \n",
       "2               3      0    Defender  \n",
       "3               7      0  Midfielder  \n",
       "4               2      0    Defender  \n",
       "..            ...    ...         ...  \n",
       "682             0      0  Goalkeeper  \n",
       "683             0      0     Forward  \n",
       "684             0      0    Defender  \n",
       "685             3      0     Forward  \n",
       "686             0      0    Defender  \n",
       "\n",
       "[687 rows x 20 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#merging elements_types_df onto main_df\n",
    "#where element type in main = id on element_type_df\n",
    "main_df = pd.merge(left=main_df,\n",
    "                   right=element_types_df[['id','singular_name']],\n",
    "                   left_on='element_type', \n",
    "                right_on='id', how='left')\n",
    "print(main_df.columns)\n",
    "# now drop the element_type and id column in main\n",
    "main_df = main_df.drop([\"id\"],axis=1)\n",
    "main_df = main_df.drop([\"element_type\"],axis=1)\n",
    "\n",
    "\n",
    "#Finally rename 'singular name' from the element_type_df to 'position'\n",
    "main_df = main_df.rename(columns = {'singular_name': 'position'})\n",
    "\n",
    "#finished:\n",
    "main_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "dcb94f3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>web_name</th>\n",
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       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Özil</td>\n",
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       "      <td>Midfielder</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>30</td>\n",
       "      <td>1210</td>\n",
       "      <td>1190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sokratis</td>\n",
       "      <td>Sokratis</td>\n",
       "      <td>48</td>\n",
       "      <td>0.1</td>\n",
       "      <td>10266</td>\n",
       "      <td>19112</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Defender</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>30</td>\n",
       "      <td>1210</td>\n",
       "      <td>1190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>David Luiz</td>\n",
       "      <td>David</td>\n",
       "      <td>54</td>\n",
       "      <td>0.9</td>\n",
       "      <td>76447</td>\n",
       "      <td>120122</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Defender</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>30</td>\n",
       "      <td>1210</td>\n",
       "      <td>1190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aubameyang</td>\n",
       "      <td>Pierre-Emerick</td>\n",
       "      <td>114</td>\n",
       "      <td>9.4</td>\n",
       "      <td>1116383</td>\n",
       "      <td>3574493</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>108</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>144.9</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>Midfielder</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>30</td>\n",
       "      <td>1210</td>\n",
       "      <td>1190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cédric</td>\n",
       "      <td>Cédric</td>\n",
       "      <td>46</td>\n",
       "      <td>0.3</td>\n",
       "      <td>29526</td>\n",
       "      <td>52537</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>29.2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Defender</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>30</td>\n",
       "      <td>1210</td>\n",
       "      <td>1190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>Söndergaard</td>\n",
       "      <td>Andreas</td>\n",
       "      <td>40</td>\n",
       "      <td>0.3</td>\n",
       "      <td>31611</td>\n",
       "      <td>15736</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Goalkeeper</td>\n",
       "      <td>Wolves</td>\n",
       "      <td>29</td>\n",
       "      <td>1170</td>\n",
       "      <td>1130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>683</th>\n",
       "      <td>Cutrone</td>\n",
       "      <td>Patrick</td>\n",
       "      <td>59</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3443</td>\n",
       "      <td>3419</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Forward</td>\n",
       "      <td>Wolves</td>\n",
       "      <td>29</td>\n",
       "      <td>1170</td>\n",
       "      <td>1130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>Lonwijk</td>\n",
       "      <td>Nigel</td>\n",
       "      <td>40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1275</td>\n",
       "      <td>981</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Defender</td>\n",
       "      <td>Wolves</td>\n",
       "      <td>29</td>\n",
       "      <td>1170</td>\n",
       "      <td>1130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>Willian José</td>\n",
       "      <td>Willian José</td>\n",
       "      <td>68</td>\n",
       "      <td>0.2</td>\n",
       "      <td>45569</td>\n",
       "      <td>36387</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>27.8</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Forward</td>\n",
       "      <td>Wolves</td>\n",
       "      <td>29</td>\n",
       "      <td>1170</td>\n",
       "      <td>1130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>686</th>\n",
       "      <td>Bueno</td>\n",
       "      <td>Hugo</td>\n",
       "      <td>40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1568</td>\n",
       "      <td>942</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Defender</td>\n",
       "      <td>Wolves</td>\n",
       "      <td>29</td>\n",
       "      <td>1170</td>\n",
       "      <td>1130</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>687 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         web_name      first_name  now_cost selected_by_percent  transfers_in  \\\n",
       "0            Özil           Mesut        67                 0.5          3441   \n",
       "1        Sokratis        Sokratis        48                 0.1         10266   \n",
       "2      David Luiz           David        54                 0.9         76447   \n",
       "3      Aubameyang  Pierre-Emerick       114                 9.4       1116383   \n",
       "4          Cédric          Cédric        46                 0.3         29526   \n",
       "..            ...             ...       ...                 ...           ...   \n",
       "682   Söndergaard         Andreas        40                 0.3         31611   \n",
       "683       Cutrone         Patrick        59                 0.0          3443   \n",
       "684       Lonwijk           Nigel        40                 0.0          1275   \n",
       "685  Willian José    Willian José        68                 0.2         45569   \n",
       "686         Bueno            Hugo        40                 0.0          1568   \n",
       "\n",
       "     transfers_out form  event_points  total_points  bonus  ... goals_scored  \\\n",
       "0            54702  0.0             0             0      0  ...            0   \n",
       "1            19112  0.0             0             0      0  ...            0   \n",
       "2           120122  1.2             0            40      0  ...            1   \n",
       "3          3574493  3.0             2           108     10  ...            9   \n",
       "4            52537  0.5             0            28      3  ...            0   \n",
       "..             ...  ...           ...           ...    ...  ...          ...   \n",
       "682          15736  0.0             0             0      0  ...            0   \n",
       "683           3419  0.0             0             2      0  ...            0   \n",
       "684            981  0.0             0             0      0  ...            0   \n",
       "685          36387  1.3             0            20      0  ...            0   \n",
       "686            942  0.0             0             0      0  ...            0   \n",
       "\n",
       "    assists  ict_index  clean_sheets  saves    position     team  played  \\\n",
       "0         0        0.0             0      0  Midfielder  Arsenal      30   \n",
       "1         0        0.0             0      0    Defender  Arsenal      30   \n",
       "2         0       41.0             3      0    Defender  Arsenal      30   \n",
       "3         1      144.9             7      0  Midfielder  Arsenal      30   \n",
       "4         1       29.2             2      0    Defender  Arsenal      30   \n",
       "..      ...        ...           ...    ...         ...      ...     ...   \n",
       "682       0        0.0             0      0  Goalkeeper   Wolves      29   \n",
       "683       0        0.2             0      0     Forward   Wolves      29   \n",
       "684       0        0.0             0      0    Defender   Wolves      29   \n",
       "685       1       27.8             3      0     Forward   Wolves      29   \n",
       "686       0        0.0             0      0    Defender   Wolves      29   \n",
       "\n",
       "    strength_overall_away strength_overall_home  \n",
       "0                    1210                  1190  \n",
       "1                    1210                  1190  \n",
       "2                    1210                  1190  \n",
       "3                    1210                  1190  \n",
       "4                    1210                  1190  \n",
       "..                    ...                   ...  \n",
       "682                  1170                  1130  \n",
       "683                  1170                  1130  \n",
       "684                  1170                  1130  \n",
       "685                  1170                  1130  \n",
       "686                  1170                  1130  \n",
       "\n",
       "[687 rows x 23 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# At the moment the team column in main_df is just a number representing the team name.\n",
    "# I will replace this column with actual team names and I while I am merging the teams_df dataFrame I will add the amount of games played by that team, and the strength of that team home and away\n",
    "\n",
    "#merging teams_df onto main_df where the team column = id column on teams_df \n",
    "main_df = pd.merge(left=main_df,\n",
    "                   right=teams_df[['id','name','played','strength_overall_away','strength_overall_home']],\n",
    "                   left_on='team', \n",
    "                   right_on='id', \n",
    "                   how='left')\n",
    "#dropping the id column added\n",
    "main_df = main_df.drop([\"id\", \"team\"],axis=1)\n",
    "#changing team name to names instead of numbers:\n",
    "main_df = main_df.rename(columns = {'name': 'team'})\n",
    "\n",
    "#scroll to right to see new columns\n",
    "main_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "8fd9cf11",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Converting some additional columns to floats incase of potential issues when doing calculations\n",
    "main_df['value'] = main_df.value_season.astype(float)\n",
    "main_df['ict_score'] = main_df.ict_index.astype(float)\n",
    "main_df['selection_percentage'] = main_df.selected_by_percent.astype(float)\n",
    "main_df['current_form'] = main_df.form.astype(float)\n",
    "\n",
    "# Creating a new column called Total Goals Contribution column which = Goals + Assists\n",
    "main_df['total_contribution']= main_df['goals_scored'] + main_df['assists']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "ff18b3b2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>web_name</th>\n",
       "      <th>first_name</th>\n",
       "      <th>now_cost</th>\n",
       "      <th>selected_by_percent</th>\n",
       "      <th>transfers_in</th>\n",
       "      <th>transfers_out</th>\n",
       "      <th>form</th>\n",
       "      <th>event_points</th>\n",
       "      <th>total_points</th>\n",
       "      <th>bonus</th>\n",
       "      <th>...</th>\n",
       "      <th>position</th>\n",
       "      <th>team</th>\n",
       "      <th>played</th>\n",
       "      <th>strength_overall_away</th>\n",
       "      <th>strength_overall_home</th>\n",
       "      <th>value</th>\n",
       "      <th>ict_score</th>\n",
       "      <th>selection_percentage</th>\n",
       "      <th>current_form</th>\n",
       "      <th>total_contribution</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>David Luiz</td>\n",
       "      <td>David</td>\n",
       "      <td>54</td>\n",
       "      <td>0.9</td>\n",
       "      <td>76447</td>\n",
       "      <td>120122</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>Defender</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>30</td>\n",
       "      <td>1210</td>\n",
       "      <td>1190</td>\n",
       "      <td>7.4</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aubameyang</td>\n",
       "      <td>Pierre-Emerick</td>\n",
       "      <td>114</td>\n",
       "      <td>9.4</td>\n",
       "      <td>1116383</td>\n",
       "      <td>3574493</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>108</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>Midfielder</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>30</td>\n",
       "      <td>1210</td>\n",
       "      <td>1190</td>\n",
       "      <td>9.5</td>\n",
       "      <td>144.9</td>\n",
       "      <td>9.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cédric</td>\n",
       "      <td>Cédric</td>\n",
       "      <td>46</td>\n",
       "      <td>0.3</td>\n",
       "      <td>29526</td>\n",
       "      <td>52537</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>Defender</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>30</td>\n",
       "      <td>1210</td>\n",
       "      <td>1190</td>\n",
       "      <td>6.1</td>\n",
       "      <td>29.2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     web_name      first_name  now_cost selected_by_percent  transfers_in  \\\n",
       "2  David Luiz           David        54                 0.9         76447   \n",
       "3  Aubameyang  Pierre-Emerick       114                 9.4       1116383   \n",
       "4      Cédric          Cédric        46                 0.3         29526   \n",
       "\n",
       "   transfers_out form  event_points  total_points  bonus  ...    position  \\\n",
       "2         120122  1.2             0            40      0  ...    Defender   \n",
       "3        3574493  3.0             2           108     10  ...  Midfielder   \n",
       "4          52537  0.5             0            28      3  ...    Defender   \n",
       "\n",
       "      team  played  strength_overall_away  strength_overall_home value  \\\n",
       "2  Arsenal      30                   1210                   1190   7.4   \n",
       "3  Arsenal      30                   1210                   1190   9.5   \n",
       "4  Arsenal      30                   1210                   1190   6.1   \n",
       "\n",
       "   ict_score  selection_percentage current_form total_contribution  \n",
       "2       41.0                   0.9          1.2                  1  \n",
       "3      144.9                   9.4          3.0                 10  \n",
       "4       29.2                   0.3          0.5                  1  \n",
       "\n",
       "[3 rows x 28 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# if the 'value' of a player is 0 (main_df.value = 0) that means they have played 0 minutes this season and will just skew the data\n",
    "# using the loc function I will filter out all players with a value of a 0:\n",
    "main_df = main_df.loc[main_df.value > 0]\n",
    "#preview to make sure that worked\n",
    "main_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "511af7d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>position</th>\n",
       "      <th>value</th>\n",
       "      <th>total_points</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "      <th>2</th>\n",
       "      <td>Goalkeeper</td>\n",
       "      <td>12.18</td>\n",
       "      <td>2270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Defender</td>\n",
       "      <td>9.49</td>\n",
       "      <td>8311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Midfielder</td>\n",
       "      <td>8.69</td>\n",
       "      <td>10615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Forward</td>\n",
       "      <td>7.34</td>\n",
       "      <td>3536</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     position  value  total_points\n",
       "2  Goalkeeper  12.18          2270\n",
       "0    Defender   9.49          8311\n",
       "3  Midfielder   8.69         10615\n",
       "1     Forward   7.34          3536"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#looking at data by position:\n",
    "\n",
    "#grouping players together by each position, and getting the mean value and total points for each player. Rounded to 2 decimal places\n",
    "position_group = np.round(main_df.groupby('position', as_index=False).\n",
    "                          aggregate({'value':np.mean, 'total_points':np.sum}), 2)\n",
    "\n",
    "position_group.sort_values('value', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "f14840f1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team</th>\n",
       "      <th>value</th>\n",
       "      <th>total_points</th>\n",
       "      <th>games_played</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aston Villa</td>\n",
       "      <td>12.82</td>\n",
       "      <td>1396</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Leeds</td>\n",
       "      <td>11.59</td>\n",
       "      <td>1286</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Man City</td>\n",
       "      <td>11.48</td>\n",
       "      <td>1778</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>West Ham</td>\n",
       "      <td>10.99</td>\n",
       "      <td>1318</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chelsea</td>\n",
       "      <td>10.48</td>\n",
       "      <td>1480</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           team  value  total_points  games_played\n",
       "1   Aston Villa  12.82          1396            29\n",
       "8         Leeds  11.59          1286            30\n",
       "11     Man City  11.48          1778            31\n",
       "18     West Ham  10.99          1318            29\n",
       "4       Chelsea  10.48          1480            30"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#looking at data by team: \n",
    "\n",
    "#grouping player together by each team, and getting the mean value and total points for each player. Rounded to 2 decimal places\n",
    "team_group = np.round(main_df.groupby('team', as_index=False).aggregate({'value':np.mean, 'total_points':np.sum}), 2)\n",
    "team_grp_df = team_group.sort_values('value', ascending=False)\n",
    "# also adding games played by each team\n",
    "team_grp_df['games_played'] = teams_df['played']\n",
    "team_grp_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "b2ee91c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team</th>\n",
       "      <th>value</th>\n",
       "      <th>total_points</th>\n",
       "      <th>games_played</th>\n",
       "      <th>value_adjusted</th>\n",
       "      <th>points_adjusted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aston Villa</td>\n",
       "      <td>12.82</td>\n",
       "      <td>1396</td>\n",
       "      <td>29</td>\n",
       "      <td>0.44</td>\n",
       "      <td>48.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Leeds</td>\n",
       "      <td>11.59</td>\n",
       "      <td>1286</td>\n",
       "      <td>30</td>\n",
       "      <td>0.39</td>\n",
       "      <td>42.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>West Ham</td>\n",
       "      <td>10.99</td>\n",
       "      <td>1318</td>\n",
       "      <td>29</td>\n",
       "      <td>0.38</td>\n",
       "      <td>45.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Man City</td>\n",
       "      <td>11.48</td>\n",
       "      <td>1778</td>\n",
       "      <td>31</td>\n",
       "      <td>0.37</td>\n",
       "      <td>57.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chelsea</td>\n",
       "      <td>10.48</td>\n",
       "      <td>1480</td>\n",
       "      <td>30</td>\n",
       "      <td>0.35</td>\n",
       "      <td>49.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           team  value  total_points  games_played  value_adjusted  \\\n",
       "1   Aston Villa  12.82          1396            29            0.44   \n",
       "8         Leeds  11.59          1286            30            0.39   \n",
       "18     West Ham  10.99          1318            29            0.38   \n",
       "11     Man City  11.48          1778            31            0.37   \n",
       "4       Chelsea  10.48          1480            30            0.35   \n",
       "\n",
       "    points_adjusted  \n",
       "1             48.14  \n",
       "8             42.87  \n",
       "18            45.45  \n",
       "11            57.35  \n",
       "4             49.33  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Not every team has played the same amount of games so it's unfair to rank teams completely on value/total_points\n",
    "# I will also rank them on value and total_points per game so for example value/games_played will be our new value metric\n",
    "\n",
    "#grouping players together again and \n",
    "team_group = np.round(main_df.groupby('team', as_index=False).aggregate({'value':np.mean, 'total_points':np.sum}), 2)\n",
    "team_grp_df = team_group\n",
    "team_grp_df['games_played'] = teams_df['played']\n",
    "# dividing value and total points by games played\n",
    "team_grp_df['value_adjusted'] = np.round(team_grp_df['value']/teams_df['played'],2)\n",
    "team_grp_df['points_adjusted'] = np.round(team_grp_df['total_points']/teams_df['played'],2)\n",
    "team_grp_df.sort_values('value_adjusted',ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "bdb3f66e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '')"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 1440x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting graphs of value and value per game (value_adjusted)\n",
    "\n",
    "# plot is 1 row and has to figures (2 columns)\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(20,7))\n",
    "plt.subplots_adjust(hspace=0.25,  wspace=0.25)\n",
    "#horizontal bar plot for each of the plots (axes[0] and axes[1])\n",
    "team_grp_df.sort_values('value').plot.barh(ax=axes[0],x=\"team\", y=\"value\", subplots=True, color='#0087F1')\n",
    "team_grp_df.sort_values('value_adjusted').plot.barh(ax=axes[1],x=\"team\", y=\"value_adjusted\", subplots=True, color='#2BBD00')\n",
    "plt.ylabel(\"\")\n",
    "\n",
    "\n",
    "\n",
    "#Plotting graphs of total points and points per game (points_adjusted)\n",
    "\n",
    "#graph has 1 row and 2 columns (2 graphs)\n",
    "fig,axes = plt.subplots(nrows=1, ncols=2, figsize=(20,7))\n",
    "plt.subplots_adjust(hspace=0.25,  wspace=0.25)\n",
    "\n",
    "#horizontal bar plot for each of the plots (axes[0] and axes[1])\n",
    "team_grp_df.sort_values('total_points').plot.barh(ax=axes[0],x=\"team\", y=\"total_points\", subplots=True, color='#EB2000')\n",
    "team_grp_df.sort_values('points_adjusted').plot.barh(ax=axes[1],x=\"team\", y=\"points_adjusted\", subplots=True, color='#FF8000')\n",
    "plt.ylabel(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "79ceee9b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>web_name</th>\n",
       "      <th>team</th>\n",
       "      <th>selection_percentage</th>\n",
       "      <th>now_cost</th>\n",
       "      <th>clean_sheets</th>\n",
       "      <th>saves</th>\n",
       "      <th>bonus</th>\n",
       "      <th>total_points</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Leno</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>6.7</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>73</td>\n",
       "      <td>8</td>\n",
       "      <td>104</td>\n",
       "      <td>20.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Ryan</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>6.0</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Rúnarsson</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     web_name     team  selection_percentage  now_cost  clean_sheets  saves  \\\n",
       "7        Leno  Arsenal                   6.7        50             8     73   \n",
       "24       Ryan  Arsenal                   6.0        42             2     23   \n",
       "30  Rúnarsson  Arsenal                   0.0        44             0      2   \n",
       "\n",
       "    bonus  total_points  value  \n",
       "7       8           104   20.8  \n",
       "24      0            29    6.9  \n",
       "30      0             1    0.2  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Team data can only tell us so much so I will create 4 different dataframe's for each position\n",
    "# And analyse stats within each position\n",
    "\n",
    "#Creating Goal Keeper DataFrame:\n",
    "\n",
    "gk_df = main_df.loc[main_df.position == 'Goalkeeper']\n",
    "gk_df = gk_df[['web_name','team','selection_percentage','now_cost','clean_sheets','saves','bonus','total_points','value']]\n",
    "gk_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "1f0b3b1d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>web_name</th>\n",
       "      <th>team</th>\n",
       "      <th>selection_percentage</th>\n",
       "      <th>now_cost</th>\n",
       "      <th>clean_sheets</th>\n",
       "      <th>assists</th>\n",
       "      <th>goals_scored</th>\n",
       "      <th>total_contribution</th>\n",
       "      <th>ict_score</th>\n",
       "      <th>bonus</th>\n",
       "      <th>total_points</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>David Luiz</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>0.9</td>\n",
       "      <td>54</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cédric</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>0.3</td>\n",
       "      <td>46</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>29.2</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Mustafi</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     web_name     team  selection_percentage  now_cost  clean_sheets  assists  \\\n",
       "2  David Luiz  Arsenal                   0.9        54             3        0   \n",
       "4      Cédric  Arsenal                   0.3        46             2        1   \n",
       "6     Mustafi  Arsenal                   0.0        50             0        0   \n",
       "\n",
       "   goals_scored  total_contribution  ict_score  bonus  total_points  value  \n",
       "2             1                   1       41.0      0            40    7.4  \n",
       "4             0                   1       29.2      3            28    6.1  \n",
       "6             0                   0        1.3      0             3    0.6  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating Defenders DataFrame:\n",
    "\n",
    "def_df = main_df.loc[main_df.position == 'Defender']\n",
    "def_df = def_df[['web_name','team','selection_percentage','now_cost','clean_sheets','assists','goals_scored','total_contribution','ict_score','bonus','total_points','value']]\n",
    "def_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "5cdb774c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>web_name</th>\n",
       "      <th>team</th>\n",
       "      <th>selection_percentage</th>\n",
       "      <th>now_cost</th>\n",
       "      <th>assists</th>\n",
       "      <th>goals_scored</th>\n",
       "      <th>total_contribution</th>\n",
       "      <th>ict_score</th>\n",
       "      <th>current_form</th>\n",
       "      <th>bonus</th>\n",
       "      <th>total_points</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aubameyang</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>9.4</td>\n",
       "      <td>114</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>144.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10</td>\n",
       "      <td>108</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Xhaka</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>0.4</td>\n",
       "      <td>52</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>72.4</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Pépé</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>1.1</td>\n",
       "      <td>76</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>93.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>63</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      web_name     team  selection_percentage  now_cost  assists  \\\n",
       "3   Aubameyang  Arsenal                   9.4       114        1   \n",
       "8        Xhaka  Arsenal                   0.4        52        2   \n",
       "15        Pépé  Arsenal                   1.1        76        2   \n",
       "\n",
       "    goals_scored  total_contribution  ict_score  current_form  bonus  \\\n",
       "3              9                  10      144.9           3.0     10   \n",
       "8              1                   3       72.4           1.2      1   \n",
       "15             5                   7       93.1           2.0      3   \n",
       "\n",
       "    total_points  value  \n",
       "3            108    9.5  \n",
       "8             57   11.0  \n",
       "15            63    8.3  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating Midfielder DataFrame:\n",
    "\n",
    "mid_df = main_df.loc[main_df.position == 'Midfielder']\n",
    "mid_df = mid_df[['web_name','team','selection_percentage','now_cost','assists','goals_scored','total_contribution','ict_score','current_form','bonus','total_points','value']]\n",
    "mid_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "8fa409a3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>web_name</th>\n",
       "      <th>team</th>\n",
       "      <th>selection_percentage</th>\n",
       "      <th>now_cost</th>\n",
       "      <th>assists</th>\n",
       "      <th>goals_scored</th>\n",
       "      <th>total_contribution</th>\n",
       "      <th>ict_score</th>\n",
       "      <th>current_form</th>\n",
       "      <th>minutes</th>\n",
       "      <th>bonus</th>\n",
       "      <th>total_points</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Lacazette</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>6.0</td>\n",
       "      <td>82</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>152.8</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1707</td>\n",
       "      <td>18</td>\n",
       "      <td>111</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Nketiah</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>0.5</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>24.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>317</td>\n",
       "      <td>4</td>\n",
       "      <td>27</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Davis</td>\n",
       "      <td>Aston Villa</td>\n",
       "      <td>7.7</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>103</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     web_name         team  selection_percentage  now_cost  assists  \\\n",
       "5   Lacazette      Arsenal                   6.0        82        3   \n",
       "18    Nketiah      Arsenal                   0.5        54        1   \n",
       "56      Davis  Aston Villa                   7.7        42        1   \n",
       "\n",
       "    goals_scored  total_contribution  ict_score  current_form  minutes  bonus  \\\n",
       "5             11                  14      152.8           5.5     1707     18   \n",
       "18             1                   2       24.9           0.0      317      4   \n",
       "56             0                   1       15.9           1.5      103      0   \n",
       "\n",
       "    total_points  value  \n",
       "5            111   13.5  \n",
       "18            27    5.0  \n",
       "56            12    2.9  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating Forward DataFrame:\n",
    "\n",
    "fwd_df = main_df.loc[main_df.position == 'Forward']\n",
    "fwd_df = fwd_df[['web_name','team','selection_percentage','now_cost','assists','goals_scored','total_contribution','ict_score','current_form','minutes','bonus','total_points','value']]\n",
    "fwd_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "1fd9f515",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now creating graphs for players in each position\n",
    "\n",
    "# Starting with the Goal Keepers, I will make a graph of cost vs points scored:\n",
    "\n",
    "#scatter plot created where x is the cost column and y is the total_points column\n",
    "ax = gk_df.plot.scatter(x='now_cost',y='total_points', alpha=.5, figsize=(20,9), title=\"goalkeepers: total_points v cost\")\n",
    "for i, txt in enumerate(gk_df.web_name):\n",
    "    ax.annotate(txt, (gk_df.now_cost.iat[i],gk_df.total_points.iat[i]))\n",
    "plt.grid(which='both', axis='both', ls='-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "decdc279",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# As there is loads of defenders compared to goal keepers I will have to filter the defenders a bit\n",
    "# Only defenders who have a value > 10 and at least 1 contribution (goal or assist) will make the graph\n",
    "\n",
    "topdef_df = def_df = def_df.loc[def_df.value > 10]\n",
    "topdef_df = topdef_df = topdef_df.loc[def_df.total_contribution > 0]\n",
    "\n",
    "#scatter plot created where x is the cost column and y is the total_points column\n",
    "ax = topdef_df.plot.scatter(x='now_cost',y='total_points', alpha=.5, figsize=(20,9), title=\"Defenders: total_points v cost\")\n",
    "for i, txt in enumerate(topdef_df.web_name):\n",
    "    ax.annotate(txt, (topdef_df.now_cost.iat[i],topdef_df.total_points.iat[i]))\n",
    "plt.grid(which='both', axis='both', ls='-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "e6257d54",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>web_name</th>\n",
       "      <th>team</th>\n",
       "      <th>selection_percentage</th>\n",
       "      <th>now_cost</th>\n",
       "      <th>clean_sheets</th>\n",
       "      <th>assists</th>\n",
       "      <th>goals_scored</th>\n",
       "      <th>total_contribution</th>\n",
       "      <th>ict_score</th>\n",
       "      <th>bonus</th>\n",
       "      <th>total_points</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>Alexander-Arnold</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>13.5</td>\n",
       "      <td>73</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>174.4</td>\n",
       "      <td>16</td>\n",
       "      <td>106</td>\n",
       "      <td>14.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>Robertson</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>13.3</td>\n",
       "      <td>71</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>169.5</td>\n",
       "      <td>13</td>\n",
       "      <td>120</td>\n",
       "      <td>16.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>Shaw</td>\n",
       "      <td>Man Utd</td>\n",
       "      <td>15.1</td>\n",
       "      <td>53</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>150.8</td>\n",
       "      <td>20</td>\n",
       "      <td>110</td>\n",
       "      <td>20.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>Cancelo</td>\n",
       "      <td>Man City</td>\n",
       "      <td>19.7</td>\n",
       "      <td>60</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>138.1</td>\n",
       "      <td>16</td>\n",
       "      <td>122</td>\n",
       "      <td>20.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>Dallas</td>\n",
       "      <td>Leeds</td>\n",
       "      <td>25.1</td>\n",
       "      <td>51</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>134.0</td>\n",
       "      <td>11</td>\n",
       "      <td>124</td>\n",
       "      <td>24.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             web_name       team  selection_percentage  now_cost  \\\n",
       "355  Alexander-Arnold  Liverpool                  13.5        73   \n",
       "352         Robertson  Liverpool                  13.3        71   \n",
       "417              Shaw    Man Utd                  15.1        53   \n",
       "383           Cancelo   Man City                  19.7        60   \n",
       "311            Dallas      Leeds                  25.1        51   \n",
       "\n",
       "     clean_sheets  assists  goals_scored  total_contribution  ict_score  \\\n",
       "355             7        4             1                   5      174.4   \n",
       "352             9        5             1                   6      169.5   \n",
       "417             9        5             1                   6      150.8   \n",
       "383            13        3             1                   4      138.1   \n",
       "311             9        3             5                   8      134.0   \n",
       "\n",
       "     bonus  total_points  value  \n",
       "355     16           106   14.5  \n",
       "352     13           120   16.9  \n",
       "417     20           110   20.8  \n",
       "383     16           122   20.3  \n",
       "311     11           124   24.3  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sometimes a defender can be playing really well and will be unlucky not to get higher points\n",
    "# I will create a second graph for defenders showing which defenders should be scoring more points (and should be considered)\n",
    "\n",
    "#ict score measures performance\n",
    "\n",
    "#quick header of top preforming players (not necessarily highest scoring!)\n",
    "unluckydef_df = def_df.loc[def_df.ict_score > 90]\n",
    "unluckydef_df.sort_values('ict_score', ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "9a7d961d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#scatter plot created where x is the ict score column and y is the total_points column\n",
    "ax = unluckydef_df.plot.scatter(x='ict_score',y='total_points', alpha=.5, figsize=(20,9), title=\"Defenders: ICT score v total points\")\n",
    "for i, txt in enumerate(unluckydef_df.web_name):\n",
    "    ax.annotate(txt, (unluckydef_df.ict_score.iat[i],unluckydef_df.total_points.iat[i]))\n",
    "plt.grid(which='both', axis='both', ls='-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "57f36352",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>web_name</th>\n",
       "      <th>team</th>\n",
       "      <th>selection_percentage</th>\n",
       "      <th>now_cost</th>\n",
       "      <th>assists</th>\n",
       "      <th>goals_scored</th>\n",
       "      <th>total_contribution</th>\n",
       "      <th>ict_score</th>\n",
       "      <th>current_form</th>\n",
       "      <th>bonus</th>\n",
       "      <th>total_points</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>Fernandes</td>\n",
       "      <td>Man Utd</td>\n",
       "      <td>58.3</td>\n",
       "      <td>116</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>29</td>\n",
       "      <td>340.1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>32</td>\n",
       "      <td>214</td>\n",
       "      <td>18.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>Salah</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>29.4</td>\n",
       "      <td>124</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>285.7</td>\n",
       "      <td>4.3</td>\n",
       "      <td>16</td>\n",
       "      <td>182</td>\n",
       "      <td>14.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>Son</td>\n",
       "      <td>Spurs</td>\n",
       "      <td>43.0</td>\n",
       "      <td>94</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>22</td>\n",
       "      <td>233.6</td>\n",
       "      <td>1.8</td>\n",
       "      <td>22</td>\n",
       "      <td>180</td>\n",
       "      <td>19.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>Rashford</td>\n",
       "      <td>Man Utd</td>\n",
       "      <td>8.7</td>\n",
       "      <td>95</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "      <td>218.6</td>\n",
       "      <td>5.3</td>\n",
       "      <td>7</td>\n",
       "      <td>148</td>\n",
       "      <td>15.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>Gündogan</td>\n",
       "      <td>Man City</td>\n",
       "      <td>34.1</td>\n",
       "      <td>61</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>181.2</td>\n",
       "      <td>2.5</td>\n",
       "      <td>21</td>\n",
       "      <td>142</td>\n",
       "      <td>23.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>Sterling</td>\n",
       "      <td>Man City</td>\n",
       "      <td>5.8</td>\n",
       "      <td>113</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "      <td>223.3</td>\n",
       "      <td>1.5</td>\n",
       "      <td>10</td>\n",
       "      <td>136</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>Mahrez</td>\n",
       "      <td>Man City</td>\n",
       "      <td>7.6</td>\n",
       "      <td>82</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>197.6</td>\n",
       "      <td>5.8</td>\n",
       "      <td>15</td>\n",
       "      <td>132</td>\n",
       "      <td>16.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Grealish</td>\n",
       "      <td>Aston Villa</td>\n",
       "      <td>17.3</td>\n",
       "      <td>75</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "      <td>249.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13</td>\n",
       "      <td>129</td>\n",
       "      <td>17.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>Ward-Prowse</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>9.8</td>\n",
       "      <td>59</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>172.9</td>\n",
       "      <td>5.5</td>\n",
       "      <td>13</td>\n",
       "      <td>128</td>\n",
       "      <td>21.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>De Bruyne</td>\n",
       "      <td>Man City</td>\n",
       "      <td>19.6</td>\n",
       "      <td>119</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>265.5</td>\n",
       "      <td>4.8</td>\n",
       "      <td>17</td>\n",
       "      <td>128</td>\n",
       "      <td>10.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>Barnes</td>\n",
       "      <td>Leicester</td>\n",
       "      <td>3.4</td>\n",
       "      <td>67</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>172.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13</td>\n",
       "      <td>126</td>\n",
       "      <td>18.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>Mané</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>4.9</td>\n",
       "      <td>118</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>240.2</td>\n",
       "      <td>4.3</td>\n",
       "      <td>8</td>\n",
       "      <td>126</td>\n",
       "      <td>10.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>Harrison</td>\n",
       "      <td>Leeds</td>\n",
       "      <td>3.0</td>\n",
       "      <td>54</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>173.6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13</td>\n",
       "      <td>124</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>Mount</td>\n",
       "      <td>Chelsea</td>\n",
       "      <td>16.7</td>\n",
       "      <td>71</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>234.6</td>\n",
       "      <td>3.3</td>\n",
       "      <td>15</td>\n",
       "      <td>123</td>\n",
       "      <td>17.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635</th>\n",
       "      <td>Soucek</td>\n",
       "      <td>West Ham</td>\n",
       "      <td>14.1</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>166.4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>15</td>\n",
       "      <td>122</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>Maddison</td>\n",
       "      <td>Leicester</td>\n",
       "      <td>4.6</td>\n",
       "      <td>71</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>169.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>12</td>\n",
       "      <td>120</td>\n",
       "      <td>16.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666</th>\n",
       "      <td>Neto</td>\n",
       "      <td>Wolves</td>\n",
       "      <td>7.1</td>\n",
       "      <td>57</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>219.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>10</td>\n",
       "      <td>118</td>\n",
       "      <td>20.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>Raphinha</td>\n",
       "      <td>Leeds</td>\n",
       "      <td>16.4</td>\n",
       "      <td>57</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>219.2</td>\n",
       "      <td>4.8</td>\n",
       "      <td>11</td>\n",
       "      <td>113</td>\n",
       "      <td>19.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>Tielemans</td>\n",
       "      <td>Leicester</td>\n",
       "      <td>3.7</td>\n",
       "      <td>65</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>150.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11</td>\n",
       "      <td>113</td>\n",
       "      <td>17.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>Pereira</td>\n",
       "      <td>West Brom</td>\n",
       "      <td>1.5</td>\n",
       "      <td>53</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>161.9</td>\n",
       "      <td>8.7</td>\n",
       "      <td>11</td>\n",
       "      <td>111</td>\n",
       "      <td>20.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>Zaha</td>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>8.7</td>\n",
       "      <td>71</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>141.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>11</td>\n",
       "      <td>109</td>\n",
       "      <td>15.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Trossard</td>\n",
       "      <td>Brighton</td>\n",
       "      <td>2.8</td>\n",
       "      <td>58</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>162.7</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13</td>\n",
       "      <td>109</td>\n",
       "      <td>18.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>Foden</td>\n",
       "      <td>Man City</td>\n",
       "      <td>7.4</td>\n",
       "      <td>61</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>151.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>12</td>\n",
       "      <td>108</td>\n",
       "      <td>17.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aubameyang</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>9.4</td>\n",
       "      <td>114</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>144.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10</td>\n",
       "      <td>108</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>632</th>\n",
       "      <td>Bowen</td>\n",
       "      <td>West Ham</td>\n",
       "      <td>1.7</td>\n",
       "      <td>59</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>141.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6</td>\n",
       "      <td>104</td>\n",
       "      <td>17.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        web_name            team  selection_percentage  now_cost  assists  \\\n",
       "418    Fernandes         Man Utd                  58.3       116       13   \n",
       "351        Salah       Liverpool                  29.4       124        4   \n",
       "553          Son           Spurs                  43.0        94        9   \n",
       "422     Rashford         Man Utd                   8.7        95        9   \n",
       "377     Gündogan        Man City                  34.1        61        3   \n",
       "382     Sterling        Man City                   5.8       113        8   \n",
       "381       Mahrez        Man City                   7.6        82        6   \n",
       "43      Grealish     Aston Villa                  17.3        75       12   \n",
       "521  Ward-Prowse     Southampton                   9.8        59        5   \n",
       "378    De Bruyne        Man City                  19.6       119       11   \n",
       "288       Barnes       Leicester                   3.4        67        5   \n",
       "348         Mané       Liverpool                   4.9       118        9   \n",
       "314     Harrison           Leeds                   3.0        54        6   \n",
       "156        Mount         Chelsea                  16.7        71        5   \n",
       "635       Soucek        West Ham                  14.1        53        1   \n",
       "285     Maddison       Leicester                   4.6        71        7   \n",
       "666         Neto          Wolves                   7.1        57        7   \n",
       "332     Raphinha           Leeds                  16.4        57        7   \n",
       "282    Tielemans       Leicester                   3.7        65        3   \n",
       "603      Pereira       West Brom                   1.5        53        6   \n",
       "184         Zaha  Crystal Palace                   8.7        71        4   \n",
       "82      Trossard        Brighton                   2.8        58        6   \n",
       "390        Foden        Man City                   7.4        61        8   \n",
       "3     Aubameyang         Arsenal                   9.4       114        1   \n",
       "632        Bowen        West Ham                   1.7        59        3   \n",
       "\n",
       "     goals_scored  total_contribution  ict_score  current_form  bonus  \\\n",
       "418            16                  29      340.1           6.0     32   \n",
       "351            18                  22      285.7           4.3     16   \n",
       "553            13                  22      233.6           1.8     22   \n",
       "422            10                  19      218.6           5.3      7   \n",
       "377            12                  15      181.2           2.5     21   \n",
       "382             9                  17      223.3           1.5     10   \n",
       "381             9                  15      197.6           5.8     15   \n",
       "43              6                  18      249.1           0.0     13   \n",
       "521             7                  12      172.9           5.5     13   \n",
       "378             5                  16      265.5           4.8     17   \n",
       "288             9                  14      172.7           0.0     13   \n",
       "348             7                  16      240.2           4.3      8   \n",
       "314             7                  13      173.6           6.0     13   \n",
       "156             6                  11      234.6           3.3     15   \n",
       "635             9                  10      166.4           2.5     15   \n",
       "285             8                  15      169.9           0.3     12   \n",
       "666             5                  12      219.9           1.7     10   \n",
       "332             6                  13      219.2           4.8     11   \n",
       "282             6                   9      150.6           3.0     11   \n",
       "603             7                  13      161.9           8.7     11   \n",
       "184             9                  13      141.3           2.3     11   \n",
       "82              4                  10      162.7           7.0     13   \n",
       "390             6                  14      151.8           3.2     12   \n",
       "3               9                  10      144.9           3.0     10   \n",
       "632             6                   9      141.1           2.8      6   \n",
       "\n",
       "     total_points  value  \n",
       "418           214   18.4  \n",
       "351           182   14.7  \n",
       "553           180   19.1  \n",
       "422           148   15.6  \n",
       "377           142   23.3  \n",
       "382           136   12.0  \n",
       "381           132   16.1  \n",
       "43            129   17.2  \n",
       "521           128   21.7  \n",
       "378           128   10.8  \n",
       "288           126   18.8  \n",
       "348           126   10.7  \n",
       "314           124   23.0  \n",
       "156           123   17.3  \n",
       "635           122   23.0  \n",
       "285           120   16.9  \n",
       "666           118   20.7  \n",
       "332           113   19.8  \n",
       "282           113   17.4  \n",
       "603           111   20.9  \n",
       "184           109   15.4  \n",
       "82            109   18.8  \n",
       "390           108   17.7  \n",
       "3             108    9.5  \n",
       "632           104   17.6  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Moving onto midfielders I will first make a data frame only keeping the highest preforming midfielders:\n",
    "\n",
    "topmid_df = mid_df.loc[mid_df.ict_score > 100]\n",
    "topmid_df = topmid_df.loc[mid_df.total_points > 100]\n",
    "topmid_df.sort_values('total_points',ascending=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "8ca250f9",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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V2KgbAAAAAAAAdiNUAgAAAAAAgN0IlQAAAAAAAGA3QiUAAAAAAADYjVAJAAAAAAAAdiNUAgAAKlSokHr16mV7bbVaVbZsWbVv3/6B5y1YsMD2eGQAAAA8XQiVAACAcuTIoSNHjig2NlaStHXrVhUsWDDNxk9KSkqzsQAAAJA1ECoBAABJkr+/vzZu3ChJWrZsmZo3b25r++2339S0aVMFBQXplVde0YkTJ2xtFy9e1JtvvqlatWppxIgRtuO+vr4aNmyYLBaL9uzZo8WLF6tx48YKDAzUwIEDlZSUpA0bNigwMFCBgYF6+eWXVb169Qy7XwAAADweQiUAACBJatasmZYvX664uDgdPnxYFStWtLX5+Pho6dKl2rBhgz744AONGTPG1nbw4EFNnTpVGzdu1IoVK3Tu3DlJ0s2bN1WxYkWFhoYqd+7cWrFihZYtW6aQkBCZzWYtWbJEQUFBCgkJUUhIiEqVKqV33nknw+8bAAAAj8YxswsAAABZQ6lSpXT27FktX75c/v7+KdquX7+uPn366NSpUzKZTEpMTLS11a5dW7ly5ZIkFS9eXOfOnVOhQoVkNpvVuHFjSdK2bdv0xx9/qFGjRpKkuLg45c2b1zbGlClT5Orqqo4dO6bzXQIAACCtECoBAPCUio63KuRklGISkmRNNhQdb1VQUJA+++wzLVq0SNeuXbP1HTdunGrWrKlZs2YpPDxcrVq1srU5OzvbfnZwcJDVapUkubi4yGw2S5IMw1Dr1q01aNCgu+rYunWrVq1apcWLF6fXrQIAACAdsPwNAICnVMjJKMUmJsnRwSRJCj0ZpTZt2qhfv34qWbJkir7R0dG2jbt/+uknu69Vu3ZtrVq1SleuXJEkXbt2TWfPntXZs2c1ZMgQTZ8+XW5ubo95RwAAAMhIzFQCAOApFZPwv0BJkm4kJMnb21tdunS5q++7776rPn36aNKkSQoICLD7WsWLF9fAgQP1xhtvyDAMOTo6auTIkdqyZYuuXbumzp07S5IKFiyoefPmPfpNAQAAIMOYDMMwMruItBIREZHZJQAA8MRYcuiqYhOT5GAyKdkw5OZkVstSeTK7LAAAAGQh3t7e921j+RsAAE+pwGKeyuFkljX5VqAUWMwzs0sCAADAE4SZSgAAAAAAALgnZioBAAAAAAAgTREqAQAyzeXLl9WjRw/VqFFDDRo0UNOmTbV27drHHjc8PFz+/v6SpH379unjjz++b9+wsDC1b9/+sa8JAAAAPG14+hsAIFMYhqHOnTurdevW+vrrryVJZ8+e1YYNG1L0s1qtcnR89P+5Kl++vMqXL/9YtQIAAAC4GzOVAACZYtu2bXJ2dk4xS+i5555T586dtWDBAnXs2FGtW7dWmzZtdPPmTfXr10+NGzdWUFCQ1q9fL+nWjKQWLVqofv36ql+/vnbt2nXXde6cibR9+3YFBgYqMDBQQUFBunHjhiTp5s2bevvtt1WnTh317NlT2Wi7QQAAACDdMFMJAJApjh07pjJlyty3/Y8//lBoaKhy586tzz//XLVq1dL48eMVFRWlxo0b6+WXX1bevHn1448/ytXVVX/++ad69OjxwOVz06ZN06hRo1SlShXFxMTIxcVFknTgwAFt2rRJBQsWVLNmzbRr1y5VrVo1ze8ZAAAAyE4IlfDU+OSTT/Tcc8/p7bffliS1bdtW3t7e+uKLLyRJw4YNU8GCBdW9e3e7xg0PD1eHDh20adOmu9r69OmjX3/9VR4eHnJwcNDIkSNVuXLlx78ZIBsaPHiwdu7cKWdnZ3Xo0EF16tRR7ty5JUlbt25VSEiIpk2bJkmKj4/XuXPnVKBAAQ0ZMkSHDh2Sg4OD/vzzzwdeo0qVKho2bJhatGihhg0b2p5kUaFCBdvPpUuXVnh4OKESAABAKhQuXFglSpSQ1WqV2WxWq1at1K1bNzk4pH5h1O0xDMOQ2WzWiBEjVKVKlXSsGmmFUAlPjSpVqmjlypV6++23lZycrMjISEVHR9vad+/erU8//fSh49i7v8tHH32kJk2aaMuWLfrwww8VGhqaoj0pKUlmsznV4wFPsuh4q0JORikmIUmXXArq9/2rbW2jRo1SZGSkGjZsKEnKkSOHrc0wDH3zzTfy8fFJMV5wcLDy5cunkJAQJScn64UXXnjg9Xv27KmAgABt2rRJzZs31w8//CBJcnZ2tvUxm82yWq2Pfa8AAABPA1dXV4WEhEiSrly5oh49eujGjRv64IMPHmmMn3/+WaNHj9bixYtT9HncfTaRPthTCU+NypUra8+ePZKko0eP6sUXX1TOnDn1999/Kz4+XidOnNDWrVvVqFEj+fv7a+DAgbZ9VVq1aqWhQ4eqYcOGmjlzpvbv3y+LxSKLxaJvv/02VdevVq2aTp06Zft55MiRql+/vlatWqVly5YpICBA/v7+GjlypCRp5cqVtpBr5syZqlGjhiTpr7/+UrNmzSTd+hJet25dWSwWffbZZ5Kkq1ev6u2331ajRo3UqFGje+4xA2SWkJNRik1MkqODSYXLVNbl6zH67rvvbO2xsbH3PM/Pz09z5syx/XfywIEDkqTr168rf/78cnBw0OLFi5WUlPTA658+fVolS5ZUjx49VL58eZ04cSKN7gwAAAB58+bV2LFjbZ/bkpKSNHz4cDVq1EgWi0Xz5s176BjR0dHy9PSUdGtvzBYtWqhjx46qW7duiif8Sre2NggODtbp06dVv3592/E///zT9rpatWr64osvVL9+fQUEBNg+/91vz07Yh5gPT42CBQvK0dFR586d0+7du1WpUiVduHBBe/bsUa5cuVSiRAl17NhRffv2lST16tVLISEhCgoKkiQlJiba9mqxWCwaMWKEqlevruHDh6fq+iEhISpZsqTtde7cubV+/XpduHBBTZs21bp16+Tp6ak33nhD69atU7Vq1TR16lRJ0o4dO5Q7d26dP39eO3bsUPXq1RUZGam1a9dq69atMplMioqKkiQNHTpUb7/9tqpWrapz586pbdu22rJlS5q9j8DjiEm4FShJktnBQW8OGa9fF36lqVOnKk+ePHJzc9PgwYMVFxeX4rw+ffrok08+kcViUXJysgoXLqy5c+eqQ4cO6tatmxYtWqR69eqlmN10LzNnzlRYWJgcHBxUvHhx1atXzxY2AwAA4PE9//zzSk5O1pUrV7R+/Xp5eHhozZo1io+PV/PmzeXn56ciRYqkOCcuLk6BgYGKj4/XpUuX9NNPP9na/vjjD23atElFihRReHj4Pa9ZtGhReXh46MCBAypTpowWLFigNm3a2Nq9vLy0fv16ffvtt5o2bZq++OILTZo06Z57dj7s8yRSIlTCU6Vy5cravXu3du/erW7duunChQvavXu3cuXKpSpVqigsLExTp05VbGys/v77b7344ou2UOmVV16RJEVFRSkqKkrVq1eXJL366qvavHnzfa85YsQITZo0SXny5LHt33TnePv27VONGjWUJ08eSVLLli3166+/qkGDBoqJidGNGzd0/vx5NW/eXDt27NDOnTvVsGFD5cqVSy4uLurfv79t1pQk/fe//9WxY8ds17lx44ZiYmLk7u6ehu8k8Gjcnc2KTUySg8mkZMPQswULqsf/h6f/dOcHATc3N40dO/auPi+88EKKJaVDhgyRdGtd/u19zmrWrKmaNWtKuvXfx3+6s12SbbYgAAAAHs+WLVt0+PBhrV59a8uD6OhonTp16q5Q6c7lb7t371bv3r1tn+UqVKhwV/97adu2rX766SeVLFlSK1eu1KpVq2xtt7dXKFeunG2iwP327PT19X3Mu366ECrhqXI7VDpy5IhKlCghb29vTZ8+XR4eHmrTpo0GDBigNWvWqFChQgoODlZ8fLzt3NQk1n379tWBAwdUsGBB29TO23sq/VNqxqtcubIWLFigF154QdWqVdN//vMf7dmzR0OHDpWjo6NWr16tbdu2afXq1ZozZ44WLlyo5ORkrVy5Uq6urna8M0DGCCzmqdCTUbqRkCR3Z7MCi3lmdkkAAABIQ3/99ZccHByUN29eSbf+Ua9u3bqpPr9y5cqKjIzU1atXJaX83mQ2m5WcnGx7fefs9kaNGmn8+PGqVauWypYtKy8vL1vb7Sf+ms1m23YJ99uzE/ZhTyVke9HxVi05dFXzfr+k616+2hASomeeeUZms1m5c+fW9evXtWfPHttT2by8vBQTE2NL0//J09NTnp6e2rlzpyRp6dKltrYJEyYoJCQkVWuFb6tQoYJ+/fVXRUZGKikpScuWLbPtn1S1alVNmzZN1atXV5kyZRQWFiZnZ2flypVLMTExio6OVkBAgD799FMdOnRI0v/2nrnt9t4zQFbg4eKoFqXyqF2F/GpZKo88XPi3DQAAgCfNnd+xrMmGouNvPeTk6tWr+vDDD9WpUyeZTCb5+flp7ty5SkxMlCSdPHlSN2/efODYJ06cUFJSku0pwHfKly+frly5osjISMXHx6eYse7q6qq6detq0KBBKWa838/99uyEffg0j2zvzo2Bcz/3gq5cjVTLFi1s7SVKlFBMTIy8vLzUtm1bBQQEKF++fCpfvvx9xxw/frz69etn+3+Uj6NAgQIaPHiwWrduLcMwFBAQkGJTuYiICFWrVk1ms1ne3t62JP3GjRvq3Lmz4uPjZRiGPvnkE0nS8OHDNXjwYFksFlmtVlWrVk1jxox5rBoBAAAA4LY7v2MlJsQrwBIkd0dDZrNZrVq1Urdu3STdWpIWHh6uevXqKSEhQUWKFNHs2bPvGu/2nkrSrRlEEydOvOcTsp2cnNS3b181adJEBQsWvGuWUYsWLbR27dpUfUe7356dsI/JuB3LZQMRERGZXQKyoHm/X7JtDCxJ1mRD7Srkz8SK8LgKFy6sEiVKyDBu/Q/XiBEjVKVKlcwuCwAAAHhiFSpUSC1bttSXX34pSbJarapYsaIqVqx4V9hiz3esc+fOadSoURo9erQ8PDzS7wZ062lw169f18CBA9P1Ok8bb2/v+7YxUwnZ3j83BnZ3vjvxxpPlzo38fv75Z40ePVqLFy9O1bmGYcgwDDk4sPoXAAAAuC1Hjhw6cuSIYmNj5ebmpq1bt6pgwYL37GvPd6xChQrp66+/Tq+ybbp06aK//vorxZPjkP74VoVsL7CYp3I4mWVNNuTmxMbA2U10dLQ8PW/9TmNiYvTaa6+pfv36CggI0Pr16yVJ4eHhevnll/X+++/L399fO3bskJ+fnwYMGKB69erpjTfeUGxsrCTp9OnTevPNN9WgQQO1aNFCJ06ckCStXLlS/v7+slgsatmyZebcLAAAAJCO/P39tXHjRknSsmXL1Lx5c1vbb7/9pqZNmyooKEiz/t1R0RfOyJpsaN+mVVoxboDefPNN1apVK8XTdrds2aKmTZuqfv366tatm2JiYtKt9lmzZik0NDTFBt1Ifyx/A/DEub38LT4+XpcuXdJPP/2kcuXKyWq1KjY2Vh4eHoqMjFTTpk21bds2nT17VjVq1NDy5ctVqVIlhYeHq1atWlqzZo3KlCmj7t27KygoSK+++qpee+01jR49Wi+88IL27t2rzz//XAsXLlRAQIDmz5+vZ599VlFRUbYgCwAAAMgOfH19tXz5ck2YMEFffvmlmjZtqmHDhmnatGmaO3euoqOj5ebmJkdHR23dulXz5s3TjBkztGDBAk2cOFHr16+Xs7Oz6tSpo6VLl8rNzU1du3bV/PnzlSNHDn399ddKSEhQ3759M/tWYSeWvwHIVu5c/rZ792717t1bmzZtkmEYGj16tHbs2CGTyaQLFy7o8uXLkqTnnntOlSpVso1RuHBhlSlTRpJUrlw5hYeHKyYmRnv27FH37t1t/RISEiTderRp37591bRpUzVs2DCjbhUAAADIMKVKldLZs2e1fPly+fv7p2i7fv26+vTpo1OnTslkMtme6CZJtWvXVq5cuSRJxYsX17lz5xQVFaVjx46pWbNmkqTExMQUn8eRPRAqAXiiVa5cWZGRkbp69ao2btyoq1evau3atXJyclK1atUUHx8v6dYa8Tu5uLjYfjabzYqLi1NycrJy5cplC6zuNGbMGO3du1cbN25Uw4YNtXbtWqbWAgAAINsJCgrSZ599pkWLFunatWu24+PGjVPNmjU1a9YshYeHq1WrVrY2Z2dn288ODg6yWq0yDEN16tTRlClTMrR+ZCz2VALwRIiOt2rJoaua9/slWZMNRcdbJUknTpxQUlKScufOrejoaOXNm1dOTk765ZdfdPbsWbuu4eHhocKFC2vlypWSbm3qffDgQUm39lp66aWXNGDAAOXJk4fltgAAAHji3eszdps2bdSvXz+VLFkyZd/oaNvG3anZDLtSpUratWuXTp06JUm6efOmTp48mfY3gUzFTCUAT4SQk1GKTUySo4NJiQnxCrAEydPVLMMwNHHiRJnNZrVs2VIdOnRQQECAypUrJx8fH7uv89VXX2nQoEGaNGmSrFarmjVrptKlS2vEiBE6deqUDMNQ7dq1Vbp06XS4SwAAACDj3PkZW5JCT0apRSlvdenS5a6+7777rvr06aNJkyYpICDgoWPnyZNHEyZMUI8ePWxbSgwcOFDFihVL25tApmKj7mzG19dXx48ft71esGCB9u/fr5EjRz7ymPv27dOiRYs0fPjwtCgReCTzfr9k+x87SbImG2pXIX8mVgQAAAA82fiMjdRgo26kmtVqlaOjY4rX5cuXV/ny5TOxKkBydzYrNjFJDiaTkg1D7s7mzC4JAAAAeKLxGRuPi1DpKbJhwwZNnjxZCQkJyp07t7766ivly5dPwcHBOn36tM6cOaNChQqpWLFiKV6/9dZbtsdIbt++XUOHDpUkmUwmLVmyRO7u7hoxYoQ2b94sk8mk999/X82aNVNYWJjGjx+v3Llz6+jRoypXrpy+/PJLmUymh1QK3C2wmKdCT0bpRkKS3J3NCizmmdklAQAAAE80PmPjcREqZTNxcXEKDAy0vf77778VFBQkSapatapWrlwpk8mkH374QVOmTNEnn3wiSTp+/LiWLl0qNzc3BQcHp3gdFhZmG2/atGkaNWqUqlSpopiYGLm4uGjNmjU6ePCgQkJCFBkZqUaNGql69eqSpAMHDmjTpk0qWLCgmjVrpl27dqlq1aoZ+I4gu/BwcVSLUnkyuwwAAAAg2+AzNh4XT3/LZlxdXRUSEmL7zwcffGBrO3/+vNq2bauAgABNnTpVx44ds7UFBQXJzc3tvq9vq1KlioYNG6ZZs2YpKipKjo6O2rlzp5o3by6z2ax8+fKpevXq2rdvnySpQoUK8vb2loODg0qXLq3w8PB0vHsg+yhUqJB69eple221WlW2bFm1b98+za+1YMECXbhwIc3HBQAAAJC9ESo9RT7++GN16tRJGzdu1JgxYxQfH29ry5EjR4q+/3x9W8+ePTVu3DjFxcWpefPmOnHixAOv6ezsbPvZbDbLarU+xh0AT48cOXLoyJEjio2NlSRt3brV9gjXtLZw4UJdvHgxXcYGAAAAkH0RKmUD0fFWLTl0VfN+vyRrsqHo+HsHN9evX7d9KV24cOEjXev06dMqWbKkevToofLly+vEiROqVq2aVqxYoaSkJF29elU7duxQhQoVHvV2APw/f39/bdy4UZK0bNkyNW/e3NZ27do1de7cWRaLRU2aNNGhQ4ckScHBwZo2bVqKMcLDwxUeHi4/Pz8NGDBA9erV0xtvvKHY2FitWrVK+/btU8+ePRUYGGgLsQAAAADgYQiVsoGQk1GKTUyyPQoy9GTUPfv1799f3bt3V4MGDeTl5fVI15o5c6b8/f1lsVjk5OSkevXqqWHDhipZsqQCAwP12muvaciQIcqfn8dQAo+rWbNmWr58ueLi4nT48GFVrFjR1hYcHKwyZcooNDRUH374oXr37v3Q8U6dOqUOHTpo8+bNypUrl9asWaMmTZqofPny+uqrrxQSEnLPZa8AAAAAcC9s1J2FTZo0ScuWLZPZbJbJZNKYMWP00ksv3dUvJuF/gdKopdt1IyHJ1tamTRu1adNGklS/fn3Vr1//rvP79+9v+7latWpau3ZtitCpZs2aqlmzpiRpxIgR96z1448/1scff5zi2J3nSdLIkSMfes8A/qdUqVI6e/asli9fLn9//xRtO3fu1IwZMyRJtWvX1rVr1xQdHf3A8QoXLqwyZcpIksqVK8ceZwAAAAAeC6FSFrV7926FhoZq3bp1cnFxUWRkpBISEu7Z193ZrNjEJDmYTEo2DLk7mzO4WgDpJSgoSJ999pkWLVqka9euPbS/2WxWcnKy7fWde6e5uLik6BcXF5e2xQIAAAB4qrD8LYu6dOmSvLy8bF8Cvby8VLBgQf33v/9VUFCQAgIC1K9fP8XHxyuwmKdGd2qsqGuRcnMyq2DMX2rVqpUkKSYmRn379lVAQIAsFotWr14tSdqyZYuaNm2q+vXrq1u3boqJiUlx/djYWL311lv6/vvvM/bGgafYvfZHa9Omjfr166eSJUum6FutWjUtWbJEkhQWFiYvLy95eHiocOHC+uOPPyRJf/zxh86cOfPQ67q7u+vGjRtpf0MAAAAAsjVCpSzKz89PERERql27tgYNGqTt27crLi5Offv21dSpU7Vx40ZZrVbNnTtXHi6OyuHkoNfK5lPLUnlSzFSaOHGiPDw8tHHjRoWGhqpWrVqKjIzUpEmTtGDBAq1fv17ly5fXN998YzsnJiZGHTt2VLNmzfTmm29mxu0DT6V77Y/m7e2tLl263NW3X79++uOPP2SxWDRq1ChNnDhRktSoUSP9/fffqlevnubMmaMXXnjhodd97bXX9OGHH7JRNwAAAAC7mAzDMDK7iLQSERGR2SWkqaSkJO3YsUNhYWGaP3++evbsqTVr1thmJ/z3v//Vd999p5kzZ6bYC2nfvn0aPny4Fi1apAYNGmjKlCkpvliGhISob9++evbZZyVJiYmJqlSpkoKDg1WtWjV5eHjovffeU8uWLTPlvoGn1bzfL9kCJUmyJhtqV4FN7wEAAABkHm9v7/u2sadSFmY2m22bXZcoUULffvvtffs6Ojra9lG5cw+VezEMQ3Xq1NGUKVPu2V6lShVt3rxZLVq0kMlkumcfAGmP/dEAAAAAPElY/paF3LmfytT1u/XHkeO2toMHD6po0aIKDw/XqVOnJEmLFy9W9erVJUnPPfec9u/fL0m2fZMkqU6dOinCqL///luVKlXSrl27bOPcvHlTJ0+etPUZMGCAnnnmGQ0ePDjd7hXA3QKLeSqHk1nWZENuTmYFFvPM7JIAAAAA4L5Y/paFLDl01TZL4cyxg1o5fawcE2/K0dFRRYsW1dixY3Xw4EENHz5cSUlJKl++vD7//HO5uLhox44d6t+/vzw8PFSjRg3t379fixYtUkxMjAYPHqw//vhDDg4O6tevnxo1aqRt27Zp1KhRtifKDRw4UEFBQbZldLlz51a/fv2UJ08effTRR5n8zgAAAAAAgMzwoOVvhEpZCPupAAAAAACArORBoRLL37IQd2ezkv8/42M/FeDpVqhQIfXq1cv22mq1qmzZsmrfvr1d47Rq1Ur79u276/iCBQs0ZMgQSdLcuXO1cOHCxysYAAAAwFOHjbqzkMBingo9GaUbCUlyd2Y/FeBpliNHDh05ckSxsbFyc3PT1q1bVbBgwXS5lr1BFQAAAABIhEpZioeLo1qUypPZZQDIIvz9/bVx40Y1adJEy5YtU/PmzbVjxw5J0m+//aahQ4cqPj5erq6uGj9+vHx8fBQbG6t+/frp0KFD8vHxUVxcnG28BQsW6Msvv5Snp6dKlSolZ2dnSVJwcLDc3d31zjvvaNasWZo3b54cHR3l6+urqVOn6tq1a+rfv7/OnDkjV1dXjR07VqVKlVJwcLDOnTunM2fO6Ny5c+ratau6dOmSKe8VAAAAgIxHqAQAWVSzZs00YcIEWSwWHT58WK+//rotVPLx8dHSpUvl6OiorVu3asyYMZoxY4bmzp0rNzc3bdmyRYcOHVKDBg0kSRcvXtQXX3yhdevWycPDQ61bt1aZMmXuuubXX3+t7du3y8XFRVFRUZJuhU5lypTR7NmztW3bNvXu3VshISGSpBMnTmjhwoWKiYnRyy+/rPbt28vJySmD3iEAAAAAmSlDQqUpU6Zo79698vT0VHBwsCRp3rx52rNnjxwdHVWgQAG99957cnd3lyQtXbpUmzZtkoODgzp16qQKFSpkRJkAkKWUKlVKZ8+e1fLly+Xv75+i7fr16+rTp49OnTolk8mkxMRESdKOHTvUuXNn2/klS5aUdGtmU40aNZQnz63ZkK+88or+/PPPu65ZsmRJ9ezZUw0aNLAFUjt37tSMGTMkSbVr19a1a9cUHR0tSQoICJCLi4tcXFyUN29eXb58+YEb+QEAAADIPjJko+66detq8ODBKY6VK1dOwcHB+uKLL/Tss89q6dKlkqSzZ88qLCxM48eP15AhQzRr1iwlJydnRJkAkKmi461acuiq5v1+SdZkQ9HxVgUFBemzzz5T8+bNU/QdN26catasqU2bNunbb79VfHx8mtQwd+5cdezYUX/88YcaNWokq9X6wP4uLi62n81ms5KSktKkDgAAAABZX4aESqVKlVLOnDlTHCtfvrzM5ltPNytevLgiIyMlSbt27VLNmjXl5OSk/Pnzq2DBgjpx4kRGlAkAmSrkZJRiE5Pk6GCSJIWejFKbNm3Ur18/24yj26Kjo20bd//000+249WqVdOyZcskSUeOHNHhw4clSRUrVtSvv/6qyMhIJSYmatWqVXddPzk5WREREapVq5aGDBmi6OhoxcTEqFq1alqyZIkkKSwsTF5eXvLw8Ejz+wcAAADwZMkSeypt2rRJNWvWlCRFRkbK19fX1ubl5WULnP4pNDRUoaGhkqTRo0enf6EAkI5iEv4XKEnSjYQkeXt733Pz63fffVd9+vTRpEmTFBAQYDvevn179evXT35+fvL19VW5cuUkSQUKFFD//v31yiuvyNPTU6VLl75rzKSkJPXq1UvR0dEyDEOdO3eWp6en+vXrp/79+8tiscjV1VUTJ05M+5sHAAAA8MQxGYZhZMSFLl26pDFjxtj2VLptyZIlOnnypD744AOZTCbNmjVLvr6+qlOnjiRp6tSpqlixoqpXr/7Qa0RERKRL7UgfhQoVUrdu3fTJJ59IkqZNm6aYmBj179//vueEhYXJyclJVapUyagygQyz5NBVxSYmycFkUrJhyM3JrJY8ERIAAABAJnrQnqkZsvztfn7++Wft2bNH77//vkymW/867+XlpatXr9r6REZGysvLK7NKRDpycXHR2rVr7zsT7V62b9+uPXv2pGNVQOYJLOapHE5mWZNvBUqBxTwzuyQAAAAAuK9MC5V+//13LV++XP/+979TbPRauXJlhYWFKTExUZcuXdL58+fl4+OTWWUiHZnNZr355pv65ptv7mq7evWq3n77bTVq1EiNGjXSrl27FB4ernnz5mnGjBkKDAzUjh07FB4ertatW8tisei1117TuXPnMuFOgLTh4eKoFqXyqF2F/GpZKo88XLLECmUAAAAAuKcMWf42ceJEHTp0SNHR0fL09NRrr72mpUuXymq12jbw9vX1Vbdu3STdWhK3efNmOTg4qGPHjqpYsWKqrsPytyeLr6+v9u7dK4vFopCQEP3www+25W89evRQhw4dVLVqVZ07d05t27bVli1bFBwcLHd3d73zzjuSpA4dOqhx48Z67bXX9J///EcbNmzQ7NmzM/nOAAAAAADIHh60/C3D9lTKCIRKTxZfX18dP35c48aNk6Ojo9zc3GyhUrly5VSgQAFb38jISG3dulXTpk1LESqVKVNGv/32m5ycnJSYmKiKFSvqwIEDmXVLAAAAAABkKw8KlVhbgQwVHW9VyMkoxSQkyZpsKDreqq5du6pBgwZq06aNrV9ycrJWrlwpV1fXTKwWAAAAAADcT6Zu1I2nT8jJKMUm/u+x6aEno5Q7d241bdpUP/74o62fn5+f5syZY3t9e/aRu7u7bty4YTteuXJlLV++XNKtZZPVqlXLiNsAAAAAAOCpR6iEDBWTcOtx6bfdSEiSJHXv3j3FU+CGDx+uffv2yWKxqG7dupo3b54kKTAwUOvWrbNt1D1ixAgtWLBAFotFixcv1meffZaxNwQAAAAAwFOKPZWQoZYcuqrYxFvBUrJx67HpLUvlyeyyAAAAAADAPTxoTyVmKiFDBRbzVA4ns6zJtwKlwGKemV0SAAAAAAB4BMxUAgAAAAAAwD0xUwkAAAAAAABpilAJAAAAAAAAdiNUAgAAAAAAgN0IlQAAAAAAAGA3QiUAAAAAAADYjVAJAAAAAAAAdiNUAgAAAAAAgN0IlQAAAAAAAGA3QiUAAAAAAADYjVAJmaZw4cIKDAyUv7+/OnTooKioqEcaJzw8XP7+/vdsGzdunLZu3frA84ODgzVt2rRHuvbTLjIyUoGBgQoMDFSFChVUqVIlBQYGytfXV4MGDXrguWFhYWrfvn0GVQoAAAAASGuOmV0Anl6urq4KCQmRJPXu3VvffvutevfunabXGDBgQJqOh5S8vLxsv8Pg4GC5u7vrnXfeyeSqAAAAAAAZgZlKyBIqVaqkCxcuSJJ+++03NW3aVEFBQXrllVd04sQJSdKCBQvUqVMntWrVSrVq1dL48eNt5yclJWnAgAGqV6+e3njjDcXGxkqS+vTpo1WrVkmSqlWrpi+++EL169dXQECAbVxJOnbsmFq1aqUaNWpo1qxZtuOdO3dWgwYNVK9ePc2fPz/d34fs4s5ZSDdv3lS/fv3UuHFjBQUFaf369Xf1v1+fBQsWqHPnznr99ddVrVo1zZkzR9OnT1dQUJCaNGmia9euSZK+//57NWrUSBaLRW+//XaK3//HH3+sV155RTVq1LD9LVy8eFEtW7a0zZTbsWNHRrwtAAAAAJCtECoh0yUlJWnbtm0KCgqSJPn4+Gjp0qXasGGDPvjgA40ZM8bW9/fff9c333yj0NBQrVq1Svv27ZMknTp1Sh06dNDmzZuVK1curVmz5p7X8vLy0vr169WuXbsUS95OnDih77//XqtXr9b48eOVmJgo6dbsm3Xr1mnNmjWaPXu2IiMj0+ttyLYmTZqkWrVqafXq1Vq4cKGGDx+umzdvprrP0aNHNXPmTK1Zs0ZjxoyRm5ubNmzYoEqVKmnRokWSpIYNG2rNmjUKDQ2Vj4+PfvzxR9vYFy9e1LJly/Tdd9/p888/lyQtXbpUfn5+CgkJUUhIiEqXLp1B7wYAAAAAZB8sf0OmiYuLU2BgoC5cuCBfX1/VqVNHknT9+nX16dNHp06dkslksgU8kvTyyy/Ly8tL0q0gYefOnWrQoIEKFy6sMmXKSJLKlSun8PDwe16zYcOGtj5r1661HQ8ICJCLi4tcXFyUN29eXb58Wd7e3po9e7atX0REhE6dOmW7PlJn69atCgkJsYV48fHxOnfuXKr71KxZUzlz5lTOnDnl4eGhwMBASVLJkiV16NAhSbeCp7Fjx+r69euKiYmRn5+fbewGDRrIwcFBxYsX1+XLlyVJFSpUUP/+/WW1WlW/fn3b3w4AAAAAIPUIlZChouOtCjkZpZiEJDk6u2jJqrVyTE5U27Zt9e2336pLly4aN26catasqVmzZik8PFytWrWynW8ymVKMd/u1i4uL7ZjZbFZcXNw9r3+7n9lsVlJS0l3H72wLCwvTf//7X61cuVJubm5q1aqV4uPjH/9NyAbu/D26O5sVWMzzvn0Nw9A333wjHx+fFMdvBzwP6rN37145OzvbXjs4ONh+VyaTyfY77Nu3r2bNmqXSpUtrwYIF2r59u+2cO883DEOSVL16dS1evFgbN25U37591a1bN7Vu3dretwEAAAAAnmosf0OGCjkZpdjEJDk63AqDQk9Gyc3NTcOHD9f06dNltVoVHR2tggULSpJ++umnFOf/97//1bVr1xQbG6v169erSpUq6VZrdHS0PD095ebmphMnTmjv3r3pdq0nzZ2/x9jEJIWevP+T+/z8/DRnzhxboHPgwIFH6vMgN27cUIECBZSYmKilS5c+tP/Zs2eVL18+vfnmm2rbtq3++OMPu64HAAAAACBUQgaLSUiSwx2zjW4k3JppUqZMGZUsWVLLli3Tu+++q88//1xBQUGyWq0pzq9QoYLefvttWSwWNWrUSOXLl0+3WuvWraukpCT5+flp1KhReumll9LtWk+aO3+PDiaT7fd4L3369FFiYqIsFovq1aunsWPHPlKfBxkwYICaNGmi5s2b3zXb6V7CwsIUGBiooKAgrVixQl27drXregAAAAAAyWTcnhqQDURERGR2CWmmcOHCKlGihJKSkuTj46NJkybJzc3tscbct2+fFi1apOHDh6dRlfZbcuiqYhNvBRLJhiE3J7NalsqTqnMXLFig/fv3a+TIkelcJR7mcX6PAAAAAIAnh7e3933bmKmURbm6uiokJESbNm2Ss7Oz5s6dm6rz/jmz507ly5e/Z6D0oHPSWmAxT+VwMsuafCuIeNBePMi6+D0CAAAAAJiplEX5+vrq+PHjkqS5c+fq8OHD+vjjj/XRRx/p6NGjSkxMVP/+/VW/fn0tWLBAa9euVUxMjJKTkzVv3rx79gsLC9O0adM0d+5cBQcH6/Tp0zpz5owKFSqkQYMG6f3337c9xn3EiBHpul8RAAAAAADI+h40U4mnv2VxVqtVmzdvVt26dTVp0iTVqlVL48ePV1RUlBo3bqyXX35ZkvTHH38oNDRUuXPn1ueff37ffnc6fvy4li5dKjc3N8XGxurHH3+Uq6ur/vzzT/Xo0UNr167N6NsFAAAAAABPCEKlLCouLk6BgYGSpGrVqumNN95Qs2bNFBISomnTpkmS4uPjde7cOUlSnTp1lDt3bknS1q1b79vvTkFBQbZ9mhITEzVkyBAdOnRIDg4O+vPPP9P9HgEAAAAAwJOLUCkLiY63KuRklGISkuTo7KIlq9bKw+V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\n",
      "text/plain": [
       "<Figure size 1440x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# graphing cost vs total points for highest preforming midfielders:\n",
    "\n",
    "#scatter plot created where x is the cost column and y is the total_points column\n",
    "ax = topmid_df.plot.scatter(x='now_cost',y='total_points', alpha=.5, figsize=(20,9), title=\"Midfielders: cost v total points\")\n",
    "for i, txt in enumerate(topmid_df.web_name):\n",
    "    ax.annotate(txt, (topmid_df.now_cost.iat[i],topmid_df.total_points.iat[i]))\n",
    "plt.grid(which='both', axis='both', ls='-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "3de6189e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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aVSaTSQ0aNNDcuXOVnJwsSTp58qRu3br10LFPnDghq9Vqe7Lg3QoVKqSrV68qOjpaiYmJqWadubq6qmHDhho8eHCqWWsP8qD9uwAAeBj+7wFAKnc2knV0MCn/06V09Vq02rZpYztftmxZxcXFydPTUx07dlRAQIAKFSqkypUrP3DMCRMmqH///rb/oU6PIkWKaMiQIWrfvr0Mw1BAQECqzVCjoqLk5+cns9ksLy8v229lb968qW7duikxMVGGYeiTTz6RJI0YMUJDhgxRYGCgLBaL/Pz8NHbs2HTVCABZ5ejRo/rzzz9ty56Rfdz972tyUqICAoPl7mjIbDarXbt26tGjh6Tby98iIyPVuHFjGYYhT09Pffvtt/eMd2ePJ+n2TKRJkybd96mvTk5O6tevn5o3b66iRYveM1upTZs2WrNmTZr+fX7Q/l0AADyMybjzK4scICoqKqtLAJ548/64LEcHkwY2e15PlfRWimGooLuLRo4cqRo1amR1eQDwxCtWrJjatm2rL7/8UtLtR95XrVpVVatWTdcP8efPn9fo0aM1ZswY5cmTJ6PKRQa58+/rHZYUQ52qFM7Cim6bMWOGbty4oUGDBmV1KQCAbMrLyytd/ZnxBCCVOxvJOjm7qO9X8+XmZJbn5f0aM2aMFi9enKYxDMOQYRhycGA1LwD8Xa5cuXTkyBHFx8fLzc1NW7ZsUdGiRdM9brFixTR16tQMqBD2cPdG7SmGIXfne2cnZbbu3bvrzz//TPVEPAAAMho/FQJIJai0h3I53f6fYTen2xvJxsbGysPj9oaycXFxeumllxQSEqKAgACtW7dOkhQZGakXXnhB7777rvz9/bV9+3Y1aNBAAwcOVKNGjfTKK68oPj5eknTmzBm9+uqraty4sdq0aaMTJ05IklasWCF/f38FBgaqbdu2WXD3AJA5/P39tWHDBknSsmXL1Lp1a9u533//XS1atFBwcLBatmxp+x45f/58vfHGG3r11VdVt27dVE9927x5s1q0aKGQkBD16NFDcXFxmXo/eLQ7/75aUgzbv69Zbfbs2QoPD0+1qTgAABmNpXYA7qt48eIqW7asEhMTdfnyZS1YsECVKlWSxWJRfHy88uTJo+joaLVo0UJbt27VuXPnVLt2bS1fvlzVqlVTZGSk6tatq9WrV8vX11dvvfWWgoOD9eKLL+qll17SmDFjVKpUKe3Zs0eff/65Fi5cqICAAP3www966qmnFBMTYwu7ACAn8fHx0fLlyzVx4kR9+eWXatGihT799FPNmDFDc+fOVWxsrNzc3OTo6KgtW7Zo3rx5+uabbzR//nxNmjRJ69atk7Ozs+rXr6+lS5fKzc1Nb7zxhn744QflypVLU6dOVVJSkvr165fVtwoAAHIAltoBsAtXV1eFhYVJknbt2qX33ntPGzdulGEYGjNmjLZv3y6TyaSLFy/qypUrkqSnn35a1apVs41RvHhx+fr6SpIqVaqkyMhIxcXFaffu3Xrrrbds7ZKSkiTdfhx0v3791KJFCzVp0iSzbhUAMl358uV17tw5LV++XP7+/qnO3bhxQ3379tXp06dlMplsTzeTpHr16ilv3rySpDJlyuj8+fOKiYnRsWPH1KpVK0lScnJyqu/FAAAAWYngCcAjVa9eXdHR0bp27Zo2bNiga9euac2aNXJycpKfn58SExMl3d635G4uLi62j81msxISEpSSkqK8efPaQq27jR07Vnv27NGGDRvUpEkTrVmzhun/AHKs4OBgffbZZ1q0aJGuX79uOz5+/HjVqVNHs2fPVmRkpNq1a2c75+zsbPvYwcFBFotFhmGofv36mjZtWqbWDwAAkBbs8QTAJjbRoiWHrmneH5dlSTEUm2iRJJ04cUJWq1X58+dXbGysChYsKCcnJ/322286d+7cY10jT548Kl68uFasWCHp9kbkBw8elHR776fnn39eAwcOVIECBVg+CyDHuN/31w4dOqh///4qV65c6raxsbbNxtOy6XO1atW0c+dOnT59WpJ069YtnTx5MuNvAgAA4B9gxhMAm7CTMYpPtsrRwaTkpEQFBAbLw9UswzA0adIkmc1mtW3bVl26dFFAQIAqVaokb2/vx77OV199pcGDB2vy5MmyWCxq1aqVKlSooJEjR+r06dMyDEP16tVThQoV7HCXAJD57v7+KknhJ2PUpryXunfvfk/bnj17qm/fvpo8ebICAgIeOXaBAgU0ceJE9e7d27Z0edCgQSpdunTG3gQAAMA/wObiAGzm/XHZ9kORJFlSDHWqUtj22sfHR8ePH7e9nj9/vvbt26dRo0b942vu3btXixYt0ogRI/7xGACQ3T3q+ysAAEB2xebiADKMu7NZ8clWOZhMSjEMuTubM3R8i8UiR0fHVK8rV66sypUrZ+h1ACC7sff3VwAAgOyK4AmATVBpD4WfjNHNJKvcnc0KKu2R5r7r16/XlClTlJSUpPz58+urr75SoUKFFBoaqjNnzujs2bMqVqyYSpcuner1a6+9ZnuE+LZt2zRs2DBJkslk0pIlS+Tu7q6RI0dq06ZNMplMevfdd9WqVStFRERowoQJyp8/v44ePapKlSrpyy+/lMlkekSlAJD50vP9FQAA4ElG8ATAJo+Lo9qUL/DA8wkJCQoKCrK9/uuvvxQcHCxJqlmzplasWCGTyaT//Oc/mjZtmj755BNJ0vHjx7V06VK5ubkpNDQ01euIiAjbeDNmzNDo0aNVo0YNxcXFycXFRatXr9bBgwcVFham6OhoNW3aVLVq1ZIkHThwQBs3blTRokXVqlUr7dy5UzVr1rTHWwMA6fKo768AAAA5FU+1A5Bmrq6uCgsLs/15//33becuXLigjh07KiAgQNOnT9exY8ds54KDg+Xm5vbA13fUqFFDn376qWbPnq2YmBg5Ojpqx44dat26tcxmswoVKqRatWpp7969kqQqVarIy8tLDg4OqlChgiIjI+149wByqmLFiumdd96xvbZYLKpYsaI6d+6c4deaP3++Ll68mOHjAgAAZFcETwAyxMcff6yuXbtqw4YNGjt2rBITE23ncuXKlart31/f0adPH40fP14JCQlq3bq1Tpw48dBrOjs72z42m82yWCzpuAMA/1a5cuXSkSNHFB8fL0nasmWLihYtapdrLVy4UJcuXbLL2AAAANkRwROAh4pNtGjJoWua98dlWVIMxSbeP9y5ceOG7Qe1hQsX/qNrnTlzRuXKlVPv3r1VuXJlnThxQn5+fvrll19ktVp17do1bd++XVWqVPmntwMA9+Xv768NGzZIkpYtW6bWrVvbzl2/fl3dunVTYGCgmjdvrkOHDkmSQkNDNWPGjFRjREZGKjIyUg0aNNDAgQPVqFEjvfLKK4qPj9fKlSu1d+9e9enTR0FBQbagCwAAICcjeALwUGEnYxSfbLU9Bjz8ZMx92w0YMEBvvfWWGjduLE9Pz390rVmzZsnf31+BgYFycnJSo0aN1KRJE5UrV05BQUF66aWXNHToUBUuzCPIAWSsVq1aafny5UpISNDhw4dVtWpV27nQ0FD5+voqPDxcH374od57771Hjnf69Gl16dJFmzZtUt68ebV69Wo1b95clStX1ldffaWwsLD7LjkGAADIadhcHMjGJk+erGXLlslsNstkMmns2LF6/vnn7XpNPz8/rVmzxhYexSX9X+g0euk23Uyy2tp26NBBHTp0kCSFhIQoJCTknvEGDBjw0Nd16tRRnTp1JEkjR468b00ff/yxPv744wf2k6RRo0al6f4A4H7Kly+vc+fOafny5fL39091bseOHfrmm28kSfXq1dP169cVGxv70PGKFy8uX19fSVKlSpXYgw4AAPxrETwB2dSuXbsUHh6utWvXysXFRdHR0UpKSsr0OtydzYpPtsrBZFKKYcjd2ZzpNQBAZggODtZnn32mRYsW6fr1649sbzablZKSYnt99952Li4uqdolJCRkbLEAAABPCJbaAdnU5cuX5enpafvhxdPTU0WLFtWvv/6q4OBgBQQEqH///rYfdPz8/BQdHS1J2rt3r9q1aydJiouLU79+/RQQEKDAwECtWrVKkrR582a1aNFCISEh6tGjh+Li4lJdPz4+Xq+99pqu71ytXE5mWVIMuTmZFVTaI7PeAgCwm/vtX9ehQwf1799f5cqVS9XWz89PS5YskSRFRETI09NTefLkUfHixbV//35J0v79+3X27NlHXtfd3V03b97M+BsCAADIpgiegGyqQYMGioqKUr169TR48GBt27ZNCQkJ6tevn6ZPn64NGzbIYrFo7ty5Dx1n0qRJypMnjzZs2KDw8HDVrVtX0dHRmjx5subPn69169apcuXK+vrrr2194uLi9Prrr6tVq1bq3qWT2pQvoE5VCqtt+QLK48JESQBPvvvtX+fl5aXu3bvf07Z///7av3+/AgMDNXr0aE2aNEmS1LRpU/31119q1KiR5syZo1KlSj3yui+99JI+/PBDNhcHAAD/GibDMIysLiKjREVFZXUJQIayWq3avn27IiIi9MMPP6hPnz5avXq17Tfvv/76q77//nvNmjUr1d5Me/fu1YgRI7Ro0SI1btxY06ZNS/UDUVhYmPr166ennnpKkpScnKxq1aopNDRUfn5+ypMnj3r16qW2bdtmyX0DgL3N++OyLXSSJEuKoU5VeHABAADA33l5eaWrP1MXgGzMbDbbNtEuW7asvvvuuwe2dXR0tO01cvc+I/djGIbq16+vadOm3fd8jRo1tGnTJrVp00Ymk+m+bQDgScb+dQAAAJmDpXZANnL3niPT1+3S/iPHbecOHjyokiVLKjIyUqdPn5YkLV68WLVq1ZIkPf3009q3b58k2fZxkqT69eunCqz++usvVatWTTt37rSNc+vWLZ08edLWZuDAgcqXL5+GDBlit3sFgKwUVNqD/esAAAAyAUvtgGxkyaFrtt/Anz12UCtmjpNj8i05OjqqZMmSGjdunA4ePKgRI0bIarWqcuXK+vzzz+Xi4qLt27drwIABypMnj2rXrq19+/Zp0aJFiouL05AhQ7R//345ODiof//+atq0qbZu3arRo0fbnpQ3aNAgBQcH25bs5c+fX/3791eBAgX00UcfZfE7AwAAAADICuldakfwBGQj7DkCAAAAAMhO0hs8sdQOyEbcnc1K+f9ZcEbtOVKsWDG98847ttcWi0UVK1ZU586dH2ucdu3aae/evfccnz9/voYOHSpJmjt3rhYuXJi+ggEAAAAAOQabiwPZSFBpD4WfjNHNJKvcnTNmz5FcuXLpyJEjio+Pl5ubm7Zs2aKiRYtmQLX3etwwCwAAAACQsxE8AdlIHhdHtSlfIMPH9ff314YNG9S8eXMtW7ZMrVu31vbt2yVJv//+u4YNG6bExES5urpqwoQJ8vb2Vnx8vPr3769Dhw7J29tbCQkJtvHmz5+vL7/8Uh4eHipfvrycnZ0lSaGhoXJ3d9fbb7+t2bNna968eXJ0dJSPj4+mT5+u69eva8CAATp79qxcXV01btw4lS9fXqGhoTp//rzOnj2r8+fP64033lD37t0z/H0AAAAAAGQugifgX6BVq1aaOHGiAgMDdfjwYb388su24Mnb21tLly6Vo6OjtmzZorFjx+qbb77R3Llz5ebmps2bN+vQoUNq3LixJOnSpUv64osvtHbtWuXJk0ft27eXr6/vPdecOnWqtm3bJhcXF8XExEi6HUz5+vrq22+/1datW/Xee+8pLCxMknTixAktXLhQcXFxeuGFF9S5c2c5OTll0jsEAAAAALCHTAmepk2bpj179sjDw0OhoaGSpHnz5mn37t1ydHRUkSJF1KtXL7m7u0uSli5dqo0bN8rBwUFdu3ZVlSpVMqNMIMcqX768zp07p+XLl8vf3z/VuRs3bqhv3746ffq0TCaTkpOTJUnbt29Xt27dbP3LlSsn6fYMqdq1a6tAgdszs1q2bKlTp07dc81y5cqpT58+aty4sS202rFjh7755htJUr169XT9+nXFxsZKkgICAuTi4iIXFxcVLFhQV65cSfcmdgAAAACArJUpm4s3bNhQQ4YMSXWsUqVKCg0N1RdffKGnnnpKS5culSSdO3dOERERmjBhgoYOHarZs2crJSUlM8oEcozYRIuWHLqmeX9cliXFUGyiRcHBwfrss8/UunXrVG3Hjx+vOnXqaOPGjfruu++UmJiYITXMnTtXr7/+uvbv36+mTZvKYrE8tL2Li4vtY7PZLKvVmiF1AAAAAACyTqYET+XLl1fu3LlTHatcubLM5ttP7CpTpoyio6MlSTt37lSdOnXk5OSkwoULq2jRojpx4kRmlAnkGGEnYxSfbJWjg0mSFH4yRh06dFD//v1tM5fuiI2NtW02vmDBAttxPz8/LVu2TJJ05MgRHT58WJJUtWpV/e9//1N0dLSSk5O1cuXKe66fkpKiqKgo1a1bV0OHDlVsbKzi4uLk5+enJUuWSJIiIiLk6empPHnyZPj9AwAAAACyh2yxx9PGjRtVp04dSVJ0dLR8fHxs5zw9PW2h1N+Fh4crPDxckjRmzBj7Fwo8IeKS/i90kqSbSVZ5eXndd8Punj17qm/fvpo8ebICAgJsxzt37qz+/furQYMG8vHxUaVKlSRJRYoU0YABA9SyZUt5eHioQoUK94xptVr1zjvvKDY2VoZhqFu3bvLw8FD//v01YMAABQYGytXVVZMmTcr4mwcAAAAAZBsmwzCMzLjQ5cuXNXbsWNseT3csWbJEJ0+e1Pvvvy+TyaTZs2fLx8dH9evXlyRNnz5dVatWVa1atR55jaioKLvUDjxplhy6pvhkqxxMJqUYhtyczGprh6flAci+ihUrph49euiTTz6RJM2YMUNxcXEaMGDAA/tERETIyclJNWrUyKwyAQAAkM2ld+/dTFlq9yD//e9/tXv3br377rsymW7PzvD09NS1a9dsbaKjo+Xp6ZlVJQJPpKDSHsrlZJYl5XboFFTaI6tLApDJXFxctGbNmgfOGr6fbdu2affu3XasCgAAAP82WRY8/fHHH1q+fLk++OCDVJsKV69eXREREUpOTtbly5d14cIFeXt7Z1WZwBMpj4uj2pQvoE5VCqtt+QLK45ItVtUCyERms1mvvvqqvv7663vOXbt2TW+++aaaNm2qpk2baufOnYqMjNS8efP0zTffKCgoSNu3b1dkZKTat2+vwMBAvfTSSzp//nwW3AkAAACeZJmy1G7SpEk6dOiQYmNj5eHhoZdeeklLly6VxWKxbTru4+OjHj16SLq9/G7Tpk1ycHDQ66+/rqpVq6bpOiy1AwDgNh8fH+3Zs0eBgYEKCwvTf/7zH9tSu969e6tLly6qWbOmzp8/r44dO2rz5s0KDQ2Vu7u73n77bUlSly5d1KxZM7300kv6+eeftX79en377bdZfGcAAADITOldapdpezxlBoInAABu8/Hx0fHjxzV+/Hg5OjrKzc3NFjxVqlRJRYoUsbWNjo7Wli1bNGPGjFTBk6+vr37//Xc5OTkpOTlZVatW1YEDB7LqlgAAAJAF0hs8sf4GAIAcIjbRorCTMYpLssqSYig20aI33nhDjRs3VocOHWztUlJStGLFCrm6umZhtQAAAPg3yNLNxQEAQMYJOxmj+GSrHB1uP7Aj/GSM8ufPrxYtWuinn36ytWvQoIHmzJlje31nFpO7u7tu3rxpO169enUtX75c0u1l8H5+fplxGwAAAMhBCJ4AAMgh4pKscvj/T4mVpJtJVknSW2+9lerpdiNGjNDevXsVGBiohg0bat68eZKkoKAgrV271ra5+MiRIzV//nwFBgZq8eLF+uyzzzL3hgAAAPDEY48nAAByiCWHrik++Xb4lGIYcnMyq235AlldFgAAAJ5g6d3jiRlPAADkEEGlPZTLySxLyu3QKai0R1aXBAAAgH85ZjwBAAAAAADgvpjxBAAAAAAAgGyJ4AkAAAAAAAB2QfAEAAAAAAAAuyB4AgAAAAAAgF0QPAEAAAAAAMAuCJ4AAAAAAABgFwRPAAAAAAAAsAuCJwAAAAAAANgFwRMAAAAAAADsguAJGS46OlpBQUEKCgpSlSpVVK1aNQUFBcnHx0eDBw9+aN+IiAh17tw5kyoFgJyjePHiCgoKkr+/v7p06aKYmJh/NE5kZKT8/f3ve278+PHasmXLQ/uHhoZqxowZ/+jaAAAAyHkcs7oA5Dyenp4KCwuTdPsHEHd3d7399ttZXBUA5Gyurq62773vvfeevvvuO7333nsZeo2BAwdm6HgAAADI+ZjxhExz92ymW7duqX///mrWrJmCg4O1bt26e9o/qM38+fPVrVs3vfzyy/Lz89OcOXM0c+ZMBQcHq3nz5rp+/bok6ccff1TTpk0VGBioN998U/Hx8ZKkvn376uOPP1bLli1Vu3ZtrVy5UpJ06dIltW3b1jZjYPv27ZnxtgBAhqtWrZouXrwoSfr999/VokULBQcHq2XLljpx4oSk299Lu3btqnbt2qlu3bqaMGGCrb/VatXAgQPVqFEjvfLKK6m+f975nunn56cvvvhCISEhCggIsI0rSceOHVO7du1Uu3ZtzZ4923a8W7duaty4sRo1aqQffvjB7u8DAAAAsh7BE7LE5MmTVbduXa1atUoLFy7UiBEjdOvWrTS3OXr0qGbNmqXVq1dr7NixcnNz0/r161WtWjUtWrRIktSkSROtXr1a4eHh8vb21k8//WQb+9KlS1q2bJm+//57ff7555KkpUuXqkGDBgoLC1NYWJgqVKiQSe8GAGQcq9WqrVu3Kjg4WJLk7e2tpUuXav369Xr//fc1duxYW9s//vhDX3/9tcLDw7Vy5Urt3btXknT69Gl16dJFmzZtUt68ebV69er7XsvT01Pr1q1Tp06dUi2vO3HihH788UetWrVKEyZMUHJysqTbs2DXrl2r1atX69tvv1V0dLS93gYAAABkEyy1Q5bYsmWLwsLCbD+oJCYm6vz582luU6dOHeXOnVu5c+dWnjx5FBQUJEkqV66cDh06JOl2ODVu3DjduHFDcXFxatCggW3sxo0by8HBQWXKlNGVK1ckSVWqVNGAAQNksVgUEhIiX19f+74JAJCBEhISFBQUpIsXL8rHx0f169eXJN24cUN9+/bV6dOnZTKZbCGQJL3wwgvy9PSUdDus37Fjhxo3bqzixYvbvgdWqlRJkZGR971mkyZNbG3WrFljOx4QECAXFxe5uLioYMGCunLliry8vPTtt9/a2kVFRen06dO26wMAACBnInhCholNtCjsZIzikqxydzYrqLTHA9sahqGvv/5a3t7eqY7fCYEe1mbPnj1ydna2vXZwcJCLi4skyWQyyWq1SpL69eun2bNnq0KFCpo/f762bdtm63N3f8MwJEm1atXS4sWLtWHDBvXr1089evRQ+/btH/dtAIBMc/f3XUdnFy1ZuUaOKcnq2LGjvvvuO3Xv3l3jx49XnTp1NHv2bEVGRqpdu3a2/iaTKdV4d17f+Z4qSWazWQkJCfe9/p12ZrPZ9r33fv2tVqsiIiL066+/asWKFXJzc1O7du2UmJiY/jcBAAAA2RpL7ZBhwk7GKD7ZKkcHk+KTrQo/+eAnKjVo0EBz5syxhT4HDhz4R20e5ubNmypSpIiSk5O1dOnSR7Y/d+6cChUqpFdffVUdO3bU/v37H+t6AJDZ7v6+K0nhJ2Pk5uamESNGaObMmbJYLIqNjVXRokUlSQsWLEjV/9dff9X169cVHx+vdevWqUaNGnarNTY2Vh4eHnJzc9OJEye0Z88eu10LAAAA2QfBEzJMXJJVDv//t+UOJpNuJlkf2LZv375KTk5WYGCgGjVqpHHjxv2jNg8zcOBANW/eXK1bt75n1tT9REREKCgoSMHBwfrll1/0xhtvPNb1ACCz3f19V5Lt+66vr6/KlSunZcuWqWfPnvr8888VHBwsi8WSqn+VKlX05ptvKjAwUE2bNlXlypXtVmvDhg1ltVrVoEEDjR49Ws8//7zdrgUAAIDsw2TcmU6SA0RFRWV1Cf9qSw5dU3zy7R+CUgxDbk5mtS1fIKvLsqvixYurbNmyslqt8vb21uTJk+Xm5pauMffu3atFixZpxIgRGVQlgJwqPd9358+fr3379mnUqFF2rhIAAABPMi8vr3T1Z8YTMkxQaQ/lcjLLknL7h5+H7fGUU7i6uiosLEwbN26Us7Oz5s6dm6Z+f591cLfKlSvfN3R6WB8A/07/xu+7AAAAeLIw4wlIBx8fHx0/flySNHfuXB0+fFgff/yxPvroIx09elTJyckaMGCAQkJCNH/+fK1Zs0ZxcXFKSUnRvHnz7tsuIiJCM2bM0Ny5cxUaGqozZ87o7NmzKlasmAYPHqx3331Xt27dkiSNHDnSrnuyAAAAAAD+3dI744mn2gEZwGKxaNOmTWrYsKEmT56sunXrasKECYqJiVGzZs30wgsvSJL279+v8PBw5c+fX59//vkD293t+PHjWrp0qdzc3BQfH6+ffvpJrq6uOnXqlHr37p3qEeYAAAAAAGQnBE9AOiQkJCgoKEiS5Ofnp1deeUWtWrVSWFiYZsyYIUlKTEzU+fPnJUn169dX/vz5JUlbtmx5YLu7BQcH2/aNSk5O1tChQ3Xo0CE5ODjo1KlTdr9HAAAAAAD+KYIn4DHFJloUdjJGcUlWOTq7aMnKNcrj8n9/lQzD0Ndff33Pk/T27NmjXLlyPbLdlStXUr2+u88333yjQoUKKSwsTCkpKSpVqlRG3hoAAAAAABmKzcWBxxR2MkbxyVY5Otx+hHn4yZhU5xs0aKA5c+bozvZpBw4cuO84aW13txs3bqhw4cJycHDQ4sWLZbVa03MrAAAAAADYFcET8Jjikm4/uvyOm0mpw5++ffsqOTlZgYGBatSokcaNG3ffcdLa7m5dunTRokWLFBgYqBMnTqSaDQUAAAAAQHbDU+2yqTVr1mjChAmpjh0+fFhz586Vv7//Pe0jIyPVpUsXbdy4MbNK/Ndacuia4pNvh08pxu1HmLctX0DR0dHq0KGDpNvL5cxmszw9PSVJq1atkrOzc5bVHBoaKnd3d7399ttZVgMAAAAA4MnDU+1yqCZNmqhJkya21z/88IOWLl2qhg0bZl1RkCQFlfZQ+MkY3Uyyyt3ZrKDSHpIkT09PhYWFSbp/0GOxWOToaP+/coZhyDAMOTgwoREAAAAAkLUInp4AJ0+e1MSJE/XLL78oPj5eXbt2VUxMjCwWiwYNGqSQkBBJktVq1cCBA7Vr1y4VLVpU3377rdzc3PTjjz/qxx9/VFJSkp599llNmTLF9pQ0PL48Lo5qU75Amtr27dtXLi4uOnjwoKpXr6527drpww8/VEJCgp555hmFhoYqX758mj17tubNmydHR0f5+Pho+vTp2rZtm4YNGyZJMplMWrJkiUwm030//5GRkerYsaOqVq2q/fv3a968eVq8eLEWLlyoggULysvLS5UqVbLn2wIAAAAAwD1YapfNJScnq2XLlnr77bfVqlUrWSwWxcfHK0+ePIqOjlaLFi20detWnTt3TnXr1tXq1avl6+urt956S8HBwXrxxRcVHR1tW/I1duxYFSpUSN26dcviO8vZ7sx4OnLkiKKjozVnzhyZzWYFBgZqxIgRql27tsaPH6/Y2Fh99tlnev7557Vt2za5uLgoJiZGHh4e6tKli/r06aMaNWooLi5OLi4ukvTAz3/t2rW1fPlyVatWTfv27VO/fv20cuVKWSwWNW7cWJ06dWKpHQAAAADgsaR3qR1rcbK58ePHq0yZMmrVqpWk28uoxowZo8DAQHXo0EEXL17UlStXJEnFixeXr6+vJKlSpUqKjIyUJB09elQNGzZUsWLFtGDBAh09evSh1/Tz81N0dLRd7qd48eIKCgqy/blT46NERkbed2+rJ0Hz5s1lNpt148YNxcTEqHbt2pKk9u3ba/v27ZKkcuXKqU+fPlq8eLFtOV6NGjX06aefavbs2YqJiZGjo+NDP/9PP/20qlWrJknavn27GjduLDc3N+XJk0dBQUFZcOcAAAAAgH87ltplM7GJFoWdjFFcklVRh/do5cpVWr9+ne38kiVLdO3aNa1Zs0ZOTk7y8/NTYmKiJNlmxEiS2WxWQkKCJKlfv3569tlnlT9/fuXLl8/WPiu4urra9kHKKe7+nN2959MdaXny3Ny5c/W///1PYWFhmjJlijZs2KA+ffooICBAGzduVOvWrfWf//xHu3fvfuDnnyfcAQAAAACyG2Y8ZTNhJ2MUn2xVUlys5o3/WC/2H6HcuXPbzsfGxqpgwYJycnLSb7/9pnPnzj1yzNjYWB0+fFhjxozRr7/+KkmKiIhQ586dbW2GDh2q+fPn215PmzZNAQEBatasmU6fPi1JWr9+vZo3b67g4GB16NDBNtMmNDRU7733ntq0aaOaNWtq9erVGjlypAICAvTqq68qOTlZkrRv3z4lJCSocePG6tixoy5duqQzZ86ofv36at68uQIDA/Xyyy8rMDBQkvTBBx+odOnSKlWqlLp27Wqr7cUXX1SzZs3k7e2tkiVL6rPPPpMkbdq0SRUrVtRzzz2nMmXKyNfXV3/88cc/+TQ8ljufM0cHk+KTrQo/GXPfdnnz5pWHh4dtltPixYtVq1YtpaSkKCoqSnXr1tXQoUMVGxuruLg4nTlzRuXKlVPv3r1VuXJlnThxIs2f/1q1amndunWKj4/XzZs3c1zYBwAAAAB4MhA8ZTNxSVY5mEzatnqhbv4VrZ8mjUi1NK1QoULau3evAgICtGjRInl7ez9yzODgYN26dUv9+/dX7ty5df369Uf2yZMnjzZs2KDXX39dn3zyiSSpZs2aWrFihdavX69WrVpp2rRptvZ//vmnFixYoDlz5uidd95RnTp1tGHDBrm6umrDhg1KTk7WRx99JOn2JugnT55UixYtVLJkSV24cEEdO3ZUeHi4EhISlC9fPkm3l4v9+OOPOnXqlCwWi27evClJunz5shwdHXXixAnNmDFDP/74o86ePat169YpNjZWmzZt0rJly3T9+nUdOnTocT8Fj+3O50ySHEwm3UyyPrDtpEmTNGLECAUGBurgwYPq16+frFar3nnnHQUEBCgkJETdunWTh4eHZs2aJX9/fwUGBsrJyUmNGjVS27Zt0/T5r1ixolq0aKGgoCC99tprqlKlij1uHQAAAACAh2KpXTbj7mxWfLJVAR26q9FL3eTmZFbbvz1B7c5+T3+3ceNG28d3byJ97do1zZo1S/Xr19fs2bN1/vz5R9bRunVr23+HDx8uSbpw4YJ69uypy5cvKykpSSVKlLC1b9SokZycnFSuXDmlpKSoUaNGkqSyZcsqMjJSJ0+e1NGjR3VnL/vcuXOrcOHCunHjhlxcXHTkyBFZrVadP39eHh4eiomJ0dWrVzVy5EjFx8crOjpapv8f7vz1119KSEhQUFCQLBaLEhISdPr0aR05ckRly5aVl5eXvLy85OnpqUuXLj3yXtPrzufMwWRSimHI3dmsAQMG3Letr6+vVq5cec/xZcuW3XNs5MiR9xxzcXHRihUr7jv23Z9/SXrvvff03nvvpeEOAAAAAACwD4KnbCaotIfCT8bo5gP2C0qrO/sOXbkWrS1bf9Phw0fk4GCS1WqVyWRSSEiI7n6g4d/3fboT8tz98ccff6wePXooODhYERERmjBhgq3Nnf2lHBwc5OjoaOvj4OCguMQkhZ+8Ls+nn1XSmRNasnKN8rjc/tK7ceOGXF1dtXHjRtWtW1fPPfecrly5osTERMXExGjmzJkqVqyYPvzwQ61evdp2vZ49e6pbt26Kjo5WkyZN1KBBA02cOFFOTk6p7sNqffDso4ySUZ8zAAAAAAByGpbaZTN5XBzVpnwBdapSWG3LF7AFNI/rzr5Dh34L1/P+zTTk+zXavn27du3apRIlSiglJUXHjh2zBTxbt25N1f+XX36x/ffOk9Ju3LihokWLSpIWLlyY5lqOX0tQniIlFBfzl1JSUhR+MkbJyck6evSo8ubNq3z58qls2bIaPHiw8uXLp1q1asnFxUUmk0mnTp1SXFycVq1aZRsvX758WrVqlW3vqOTkZN26dUvPPfecbYbTsWPH0rSkMCNk1OcMAAAAAICchp+Qc6i4pNubXf++ea0ate+aat+hpk2bavny5WrRooX8/f1VokQJ+fr6puofExOjwMBAOTs7a+rUqZKkAQMG6K233pKHh4fq1q2ryMjINNWSZDHk7OyszkPHa9K7HTWyx4ua4mTSG2+8oeeee06TJk3SO++8o2vXrikuLk4jR46Uh4eH2rdvr06dOsnR0VElSpTQ1atXJUmFCxdWiRIl1LhxY1ksFv3111+yWCxq3Lix1q1bp4YNG8rb21v58uWTq6trBr2jAAAAAADgcZmMu9dbPeGioqKyuoRsY8mha6n2HbrfXlHZqZYZM2boxo0bGjRo0D++jtVqVXJyslxdXXXmzBm9/PLL2rJli5ydndN7CwAAAAAA/Ct5eXmlqz8znnKo7LTv0KNq6d69u+2peOkRHx///9q796CozvuP45+zy80Vs3KTiJfGC1ajTixFQa2XRLDT1OkkHXU0nTA2maEtiak6UaudqbU1ra2lUCpMnNHYxmYSYyOY2E7iXarxQvDSBDVETdSOCizLTYlFdp/+kV/2p5VtDLCswPv1lx7PWb5nPx6f43ef56xmzZrlW4L3q1/9iqYTAAAAAABBxIwnAAAAAAAAtKitM554uDgAAAAAAAACgsYTAAAAAAAAAoLGEwAAAAAAAAKCxhMAAAAAAAACgsYTAAAAAAAAAiIk2AWgZQMGDNDw4cNljJHdbteqVas0duzYYJcFAAAAAABw12g83aMiIiK0c+dOSdK+ffu0evVqvfHGG0GuCgAAAAAA4O6x1K4TaGhokNPplCQZY/TLX/5SjzzyiKZNm6Zt27ZJkpYvX64dO3ZIkp5++mktWrRIkvTaa69p9erVkqQ33nhD3/72t5Wenq4lS5bI4/FIkhI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      "text/plain": [
       "<Figure size 1440x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Again I will try to scout out any players who haven't got as many points as they should have\n",
    "# I will plot ict score vs total points scored to see if I can pick any players who may be overlooked by people\n",
    "\n",
    "# graphing ict score vs total points for highest preforming midfielders:\n",
    "\n",
    "#scatter plot created where x is the ict score column and y is the total_points column\n",
    "ax = topmid_df.plot.scatter(x='ict_score',y='total_points', alpha=.5, figsize=(20,9), title=\"Midfielders: ICT score v total points\")\n",
    "for i, txt in enumerate(topmid_df.web_name):\n",
    "    ax.annotate(txt, (topmid_df.ict_score.iat[i],topmid_df.total_points.iat[i]))\n",
    "plt.grid(which='both', axis='both', ls='-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "50f6d46b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>web_name</th>\n",
       "      <th>team</th>\n",
       "      <th>selection_percentage</th>\n",
       "      <th>now_cost</th>\n",
       "      <th>assists</th>\n",
       "      <th>goals_scored</th>\n",
       "      <th>total_contribution</th>\n",
       "      <th>ict_score</th>\n",
       "      <th>current_form</th>\n",
       "      <th>minutes</th>\n",
       "      <th>bonus</th>\n",
       "      <th>total_points</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>551</th>\n",
       "      <td>Kane</td>\n",
       "      <td>Spurs</td>\n",
       "      <td>47.4</td>\n",
       "      <td>116</td>\n",
       "      <td>13</td>\n",
       "      <td>19</td>\n",
       "      <td>32</td>\n",
       "      <td>287.2</td>\n",
       "      <td>10.2</td>\n",
       "      <td>2453</td>\n",
       "      <td>35</td>\n",
       "      <td>204</td>\n",
       "      <td>17.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>Bamford</td>\n",
       "      <td>Leeds</td>\n",
       "      <td>48.4</td>\n",
       "      <td>67</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>23</td>\n",
       "      <td>223.9</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2499</td>\n",
       "      <td>19</td>\n",
       "      <td>158</td>\n",
       "      <td>23.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>Vardy</td>\n",
       "      <td>Leicester</td>\n",
       "      <td>18.5</td>\n",
       "      <td>101</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>24</td>\n",
       "      <td>187.7</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2135</td>\n",
       "      <td>19</td>\n",
       "      <td>149</td>\n",
       "      <td>14.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>Calvert-Lewin</td>\n",
       "      <td>Everton</td>\n",
       "      <td>36.5</td>\n",
       "      <td>76</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>192.7</td>\n",
       "      <td>2.7</td>\n",
       "      <td>2151</td>\n",
       "      <td>16</td>\n",
       "      <td>136</td>\n",
       "      <td>17.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Watkins</td>\n",
       "      <td>Aston Villa</td>\n",
       "      <td>26.1</td>\n",
       "      <td>66</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "      <td>240.1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2610</td>\n",
       "      <td>10</td>\n",
       "      <td>127</td>\n",
       "      <td>19.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>Wilson</td>\n",
       "      <td>Newcastle</td>\n",
       "      <td>5.3</td>\n",
       "      <td>65</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>134.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1812</td>\n",
       "      <td>19</td>\n",
       "      <td>116</td>\n",
       "      <td>17.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Lacazette</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>6.0</td>\n",
       "      <td>82</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>152.8</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1707</td>\n",
       "      <td>18</td>\n",
       "      <td>111</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>Adams</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>5.4</td>\n",
       "      <td>58</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>160.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2165</td>\n",
       "      <td>11</td>\n",
       "      <td>109</td>\n",
       "      <td>18.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>Werner</td>\n",
       "      <td>Chelsea</td>\n",
       "      <td>11.1</td>\n",
       "      <td>92</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>185.6</td>\n",
       "      <td>2.7</td>\n",
       "      <td>2129</td>\n",
       "      <td>7</td>\n",
       "      <td>106</td>\n",
       "      <td>11.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>517</th>\n",
       "      <td>Ings</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>6.0</td>\n",
       "      <td>84</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>152.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1770</td>\n",
       "      <td>17</td>\n",
       "      <td>106</td>\n",
       "      <td>12.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>Firmino</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>3.0</td>\n",
       "      <td>90</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>207.3</td>\n",
       "      <td>0.7</td>\n",
       "      <td>2222</td>\n",
       "      <td>10</td>\n",
       "      <td>104</td>\n",
       "      <td>11.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Maupay</td>\n",
       "      <td>Brighton</td>\n",
       "      <td>3.4</td>\n",
       "      <td>61</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>175.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>2136</td>\n",
       "      <td>12</td>\n",
       "      <td>99</td>\n",
       "      <td>16.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>Jesus</td>\n",
       "      <td>Man City</td>\n",
       "      <td>3.0</td>\n",
       "      <td>92</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>143.4</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1565</td>\n",
       "      <td>16</td>\n",
       "      <td>97</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>Richarlison</td>\n",
       "      <td>Everton</td>\n",
       "      <td>9.5</td>\n",
       "      <td>80</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>140.4</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1984</td>\n",
       "      <td>15</td>\n",
       "      <td>94</td>\n",
       "      <td>11.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>Antonio</td>\n",
       "      <td>West Ham</td>\n",
       "      <td>14.1</td>\n",
       "      <td>67</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>148.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1509</td>\n",
       "      <td>13</td>\n",
       "      <td>89</td>\n",
       "      <td>13.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>Martial</td>\n",
       "      <td>Man Utd</td>\n",
       "      <td>3.5</td>\n",
       "      <td>86</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>126.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1477</td>\n",
       "      <td>3</td>\n",
       "      <td>75</td>\n",
       "      <td>8.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          web_name         team  selection_percentage  now_cost  assists  \\\n",
       "551           Kane        Spurs                  47.4       116       13   \n",
       "313        Bamford        Leeds                  48.4        67        9   \n",
       "278          Vardy    Leicester                  18.5       101       12   \n",
       "215  Calvert-Lewin      Everton                  36.5        76        6   \n",
       "61         Watkins  Aston Villa                  26.1        66        6   \n",
       "473         Wilson    Newcastle                   5.3        65        6   \n",
       "5        Lacazette      Arsenal                   6.0        82        3   \n",
       "527          Adams  Southampton                   5.4        58        6   \n",
       "153         Werner      Chelsea                  11.1        92        9   \n",
       "517           Ings  Southampton                   6.0        84        4   \n",
       "346        Firmino    Liverpool                   3.0        90        6   \n",
       "81          Maupay     Brighton                   3.4        61        2   \n",
       "388          Jesus     Man City                   3.0        92        4   \n",
       "217    Richarlison      Everton                   9.5        80        5   \n",
       "624        Antonio     West Ham                  14.1        67        5   \n",
       "419        Martial      Man Utd                   3.5        86        7   \n",
       "\n",
       "     goals_scored  total_contribution  ict_score  current_form  minutes  \\\n",
       "551            19                  32      287.2          10.2     2453   \n",
       "313            14                  23      223.9           4.0     2499   \n",
       "278            12                  24      187.7           5.7     2135   \n",
       "215            14                  20      192.7           2.7     2151   \n",
       "61             11                  17      240.1           4.0     2610   \n",
       "473            10                  16      134.7           0.0     1812   \n",
       "5              11                  14      152.8           5.5     1707   \n",
       "527             7                  13      160.0           5.2     2165   \n",
       "153             5                  14      185.6           2.7     2129   \n",
       "517             9                  13      152.3           3.0     1770   \n",
       "346             6                  12      207.3           0.7     2222   \n",
       "81              8                  10      175.0           4.2     2136   \n",
       "388             8                  12      143.4           4.2     1565   \n",
       "217             6                  11      140.4           1.3     1984   \n",
       "624             7                  12      148.2           2.8     1509   \n",
       "419             4                  11      126.8           1.7     1477   \n",
       "\n",
       "     bonus  total_points  value  \n",
       "551     35           204   17.6  \n",
       "313     19           158   23.6  \n",
       "278     19           149   14.8  \n",
       "215     16           136   17.9  \n",
       "61      10           127   19.2  \n",
       "473     19           116   17.8  \n",
       "5       18           111   13.5  \n",
       "527     11           109   18.8  \n",
       "153      7           106   11.5  \n",
       "517     17           106   12.6  \n",
       "346     10           104   11.6  \n",
       "81      12            99   16.2  \n",
       "388     16            97   10.5  \n",
       "217     15            94   11.8  \n",
       "624     13            89   13.3  \n",
       "419      3            75    8.7  "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now finally onto forwards... I will first filter out all the forwards who haven't yet made 8 goal contributions this season\n",
    "\n",
    "topfwd_df = fwd_df[fwd_df.total_contribution > 8]\n",
    "topfwd_df.sort_values('total_points',ascending=False)                \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "0b0b97b3",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1440x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# graphing cost vs total points for highest preforming forwards:\n",
    "\n",
    "#scatter plot created where x is the cost column and y is the total_points column\n",
    "ax = topfwd_df.plot.scatter(x='now_cost',y='total_points', alpha=.5, figsize=(20,9), title=\"Forwards: cost vs total points\")\n",
    "for i, txt in enumerate(topfwd_df.web_name):\n",
    "    ax.annotate(txt, (topfwd_df.now_cost.iat[i],topfwd_df.total_points.iat[i]))\n",
    "plt.grid(which='both', axis='both', ls='-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "40048151",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Again I will check if there is any unlucky forwards who are due a goal by comparing ICT score and total points\n",
    "\n",
    "#scatter plot created where x is the ICT score column and y is the total_points column\n",
    "ax = topfwd_df.plot.scatter(x='ict_score',y='total_points', alpha=.5, figsize=(20,9), title=\"Forwards: ICT score v total points\")\n",
    "for i, txt in enumerate(topfwd_df.web_name):\n",
    "    ax.annotate(txt, (topfwd_df.ict_score.iat[i],topfwd_df.total_points.iat[i]))\n",
    "plt.grid(which='both', axis='both', ls='-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "3871fe35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Finally I will plot the top 5 value players per position\n",
    "\n",
    "#Using nlargest() on dataframe's I will create 5 different dataframes with each one having the top value players in each position\n",
    "\n",
    "top5_gk_df = gk_df.nlargest(5, 'value')\n",
    "top5_def_df = def_df.nlargest(5, 'value')\n",
    "top5_mid_df = mid_df.nlargest(5, 'value')\n",
    "top5_fwd_df = fwd_df.nlargest(5, 'value')\n",
    "\n",
    "#Plotting value vs total points\n",
    "\n",
    "#Note ax is assigned here, however ax=ax will be used as argument for remaining scatter plots so all points will be on one graph\n",
    "ax = top5_gk_df.plot.scatter(x='value', y='total_points', color='DarkBlue', label='GK', s=top5_gk_df['value']*10, alpha=.5, figsize=(15,9), title=\"Top 5 Value Players by Position\")\n",
    "for i, txt in enumerate(top5_gk_df.web_name):\n",
    "    ax.annotate(txt, (top5_gk_df.value.iat[i],top5_gk_df.total_points.iat[i]))\n",
    "    \n",
    "top5_def_df.plot.scatter(x='value', y='total_points', color='DarkGreen', label='DEF', s=top5_gk_df['value']*10, ax=ax)\n",
    "for i, txt in enumerate(top5_def_df.web_name):\n",
    "    ax.annotate(txt, (top5_def_df.value.iat[i],top5_def_df.total_points.iat[i]))\n",
    "    \n",
    "top5_mid_df.plot.scatter(x='value', y='total_points', color='DarkOrange', label='MID', s=top5_gk_df['value']*10, ax=ax)\n",
    "for i, txt in enumerate(top5_mid_df.web_name):\n",
    "    ax.annotate(txt, (top5_mid_df.value.iat[i],top5_mid_df.total_points.iat[i]))\n",
    "    \n",
    "top5_fwd_df.plot.scatter(x='value', y='total_points', color='DarkRed', label='FWD', s=top5_gk_df['value']*10, ax=ax)\n",
    "for i, txt in enumerate(top5_fwd_df.web_name):\n",
    "    ax.annotate(txt, (top5_fwd_df.value.iat[i],top5_fwd_df.total_points.iat[i]))    "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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